Date: (Fri) May 27, 2016

Introduction:

Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Classification: Plot AUC Curves for all models & highlight glbMdlSel
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")
## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
    # or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    )                   
 
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv") 

glbObsDropCondition <- NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "Party"

# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "R"))
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
    sapply(levels(var)[as.numeric(var)], function(elm) 
        if (is.na(elm)) return(elm) else
        if (elm == 'R') return("Republican") else
        if (elm == 'D') return("Democrat") else
        stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
        )  
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & 
#       work each one in
    , "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel" 
    ,"Q124742","Q124122" 
    ,"Q123621","Q123464"
    ,"Q122771","Q122770","Q122769","Q122120"
    ,"Q121700","Q121699","Q121011"
    ,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" # Done
    ,"Q119851","Q119650","Q119334"
    ,"Q118892","Q118237","Q118233","Q118232","Q118117"
    ,"Q117193","Q117186"
    ,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
    ,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
    ,"Q114961","Q114748","Q114517","Q114386","Q114152"
    ,"Q113992","Q113583","Q113584","Q113181"
    ,"Q112478","Q112512","Q112270"
    ,"Q111848","Q111580","Q111220"
    ,"Q110740"
    ,"Q109367","Q109244"
    ,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
    ,"Q107869","Q107491"
    ,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
    ,"Q105840","Q105655"
    ,"Q104996"
    ,"Q103293"
    ,"Q102906","Q102674","Q102687","Q102289","Q102089"
    ,"Q101162","Q101163","Q101596"
    ,"Q100689","Q100680","Q100562","Q100010"
    ,"Q99982"
    ,"Q99716"
    ,"Q99581"
    ,"Q99480"
    ,"Q98869"
    ,"Q98578"
    ,"Q98197"
    ,"Q98059","Q98078"
    ,"Q96024"
    ,".pos") 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) } 
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(raw1) { return(1:length(raw1)) }
    , args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(raw1) { return(1:length(raw1)) }       
#     , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# YOB options:
# 1. Missing data:
# 1.1   0 -> Does not improve baseline
# 1.2   Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        # raw[!is.na(raw) & raw >= 2010] <- NA
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        retVal <- rep_len("NA", length(raw))
        # breaks = c(1879, seq(1949, 1989, 10), 2049)
        # cutVal <- cut(raw[!is.na(raw)], breaks = breaks, 
        #               labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
        cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
        retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
        return(factor(retVal, levels = c("NA"
                ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
                        ordered = TRUE))
    }
    , args = c("YOB"))

glbFeatsDerive[["Gender.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- raw1
        raw[raw %in% ""] <- "N"
        raw <- gsub("Male"  , "M", raw, fixed = TRUE)
        raw <- gsub("Female", "F", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("Gender"))

glbFeatsDerive[["Income.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("under $25,000"      , "<25K"    , raw, fixed = TRUE)
        raw <- gsub("$25,001 - $50,000"  , "25-50K"  , raw, fixed = TRUE)
        raw <- gsub("$50,000 - $74,999"  , "50-75K"  , raw, fixed = TRUE)
        raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)        
        raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
        raw <- gsub("over $150,000"      , ">150K"   , raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
                      ordered = TRUE))
    }
    , args = c("Income"))

glbFeatsDerive[["Hhold.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
        raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)        
        raw <- gsub("Married (no kids)"          , "MKn", raw, fixed = TRUE)
        raw <- gsub("Married (w/kids)"           , "MKy", raw, fixed = TRUE)        
        raw <- gsub("Single (no kids)"           , "SKn", raw, fixed = TRUE)
        raw <- gsub("Single (w/kids)"            , "SKy", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("HouseholdStatus"))

glbFeatsDerive[["Edn.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Current K-12"         , "K12", raw, fixed = TRUE)
        raw <- gsub("High School Diploma"  , "HSD", raw, fixed = TRUE)        
        raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
        raw <- gsub("Associate's Degree"   , "Ast", raw, fixed = TRUE)
        raw <- gsub("Bachelor's Degree"    , "Bcr", raw, fixed = TRUE)        
        raw <- gsub("Master's Degree"      , "Msr", raw, fixed = TRUE)
        raw <- gsub("Doctoral Degree"      , "PhD", raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
                      ordered = TRUE))
    }
    , args = c("EducationLevel"))

# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q1", glbFeatsExclude, fixed = TRUE, value = TRUE))    
    glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
        mapfn = function(raw1) {
            raw1[raw1 %in% ""] <- "NA"
            rawVal <- unique(raw1)
            
            if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
                raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
                raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
                raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            }
            
            return(relevel(as.factor(raw1), ref = "NA"))
        }
        , args = c(qsn))

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))

#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))

#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]])))); 
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)

#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]

# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()

# Add xgboost algorithm

# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
            ,"xgbLinear","xgbTree"
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            ,"xgbLinear","xgbTree"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart","xgbLinear","xgbTree"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial)
    # glm does not work for multinomial
    glbMdlFamilies[["All.X"]] <- c("glmnet") else    
    glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")

#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
                        ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
                        ,data.frame(parameter = "lambda", vals = "9.342e-02")    
                        )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "<mdlId>"),
#                                      glmnetTuneParams))

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
    
glb_preproc_methods <- NULL
#     c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
#     require(tidyr)
#     obsOutFinDf <- obsOutFinDf %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsOutFinDf, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsOutFinDf) {
#                   }
                  )
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
#     txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    # glbObsOut$vars[["Probability1"]] <- 
    #     "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]" 
    # glbObsOut$vars[[glb_rsp_var_raw]] <-
    #     "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
    #                                         mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
    glbObsOut$vars[["Predictions"]] <-
        "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
                                            mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- NULL #: default
    # c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack

glbOut <- list(pfx = "Votes_Q1_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- NULL #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #"extract.features.end" #NULL #default: script will save envir at end of this chunk 
#mysavChunk(glbOut$pfx, glbChunks[["last"]])

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#load("Votes_Q1_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, "import.data")
##         label step_major step_minor label_minor   bgn end elapsed
## 1 import.data          1          0           0 10.21  NA      NA

Step 1.0: import data

chunk option: eval=

## [1] "Reading file ./data/train2016.csv..."
## [1] "dimensions of data in ./data/train2016.csv: 5,568 rows x 108 cols"
##   USER_ID  YOB Gender              Income            HouseholdStatus
## 1       1 1938   Male                               Married (w/kids)
## 2       4 1970 Female       over $150,000 Domestic Partners (w/kids)
## 3       5 1997   Male  $75,000 - $100,000           Single (no kids)
## 4       8 1983   Male $100,001 - $150,000           Married (w/kids)
## 5       9 1984 Female   $50,000 - $74,999           Married (w/kids)
## 6      10 1997 Female       over $150,000           Single (no kids)
##        EducationLevel      Party Q124742 Q124122 Q123464 Q123621 Q122769
## 1                       Democrat      No              No      No      No
## 2   Bachelor's Degree   Democrat             Yes      No      No      No
## 3 High School Diploma Republican             Yes     Yes      No        
## 4   Bachelor's Degree   Democrat      No     Yes      No     Yes      No
## 5 High School Diploma Republican      No     Yes      No      No      No
## 6        Current K-12   Democrat                                      No
##   Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1     Yes  Public      No     Yes      No              No      No     Yes
## 2     Yes  Public      No     Yes      No     Yes      No      No     Yes
## 3     Yes Private      No      No      No     Yes      No      No     Yes
## 4      No  Public      No     Yes      No     Yes      No      No     Yes
## 5     Yes  Public      No     Yes      No     Yes     Yes      No     Yes
## 6     Yes  Public      No      No      No     Yes      No     Yes     Yes
##   Q120472     Q120194 Q120012 Q120014 Q119334 Q119851   Q119650 Q118892
## 1           Try first      No      No             Yes               Yes
## 2 Science Study first     Yes     Yes      No      No Receiving      No
## 3 Science Study first             Yes      No     Yes Receiving      No
## 4 Science   Try first      No     Yes     Yes      No    Giving     Yes
## 5     Art   Try first     Yes      No      No      No    Giving      No
## 6 Science   Try first     Yes     Yes      No     Yes Receiving      No
##   Q118117    Q118232 Q118233 Q118237     Q117186        Q117193 Q116797
## 1     Yes   Idealist      No      No                                Yes
## 2      No Pragmatist      No      No Cool headed Standard hours      No
## 3     Yes Pragmatist      No     Yes Cool headed      Odd hours      No
## 4      No   Idealist      No      No Cool headed Standard hours      No
## 5      No   Idealist     Yes     Yes  Hot headed Standard hours      No
## 6      No Pragmatist      No      No             Standard hours        
##   Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1   Happy     Yes     Yes      No      No    P.M.     Yes   Start     Yes
## 2   Happy     Yes     Yes     Yes      No    A.M.      No     End     Yes
## 3   Right     Yes      No      No     Yes    A.M.     Yes   Start     Yes
## 4   Happy     Yes     Yes      No      No    A.M.     Yes   Start     Yes
## 5   Happy     Yes     Yes      No     Yes    P.M.      No     End      No
## 6                                                                        
##   Q115611       Q115899 Q115390 Q114961 Q114748 Q115195 Q114517    Q114386
## 1      No Circumstances     Yes     Yes     Yes     Yes      No           
## 2      No            Me     Yes     Yes      No     Yes      No Mysterious
## 3     Yes Circumstances      No     Yes      No     Yes     Yes Mysterious
## 4      No Circumstances     Yes      No      No     Yes      No        TMI
## 5      No            Me      No     Yes     Yes     Yes     Yes        TMI
## 6                                                                         
##   Q113992 Q114152 Q113583    Q113584 Q113181 Q112478 Q112512 Q112270
## 1     Yes     Yes    Talk Technology      No      No     Yes        
## 2      No      No                                                   
## 3      No      No   Tunes Technology     Yes     Yes     Yes     Yes
## 4      No      No    Talk     People      No     Yes     Yes     Yes
## 5     Yes      No   Tunes     People      No      No     Yes      No
## 6                                                                   
##   Q111848    Q111580 Q111220 Q110740 Q109367       Q108950 Q109244 Q108855
## 1      No  Demanding      No              No      Cautious      No    Yes!
## 2                                Mac     Yes      Cautious      No  Umm...
## 3      No Supportive      No      PC      No      Cautious      No  Umm...
## 4     Yes Supportive      No     Mac     Yes Risk-friendly      No  Umm...
## 5      No  Demanding     Yes      PC     Yes      Cautious      No    Yes!
## 6     Yes Supportive      No      PC                                      
##   Q108617   Q108856 Q108754   Q108342 Q108343 Q107869 Q107491 Q106993
## 1      No     Space      No In-person             Yes      No     Yes
## 2      No     Space     Yes In-person      No     Yes     Yes      No
## 3      No     Space      No In-person      No      No     Yes     Yes
## 4      No Socialize     Yes    Online      No     Yes      No     Yes
## 5      No Socialize      No    Online      No      No     Yes     Yes
## 6                           In-person      No      No     Yes     Yes
##       Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1 Yay people!     Yes      No     Yes     Yes              No     Yes
## 2 Yay people!     Yes     Yes     Yes     Yes     Yes      No     Yes
## 3 Grrr people     Yes      No      No      No      No      No      No
## 4 Grrr people      No      No     Yes     Yes      No     Yes     Yes
## 5 Yay people!     Yes      No     Yes     Yes     Yes     Yes      No
## 6 Grrr people     Yes      No     Yes     Yes      No      No     Yes
##   Q103293 Q102906 Q102674 Q102687 Q102289 Q102089   Q101162 Q101163
## 1      No      No      No     Yes      No     Own  Optimist        
## 2                                                                  
## 3     Yes      No      No     Yes      No     Own Pessimist     Mom
## 4      No      No      No     Yes     Yes     Own  Optimist     Mom
## 5      No      No     Yes      No      No     Own  Optimist     Mom
## 6     Yes     Yes      No     Yes                                  
##   Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1     Yes     Yes      No      No   Nope     Yes     No     No       
## 2                                                                  No
## 3      No      No      No      No   Nope     Yes     No     No     No
## 4      No      No      No     Yes Check!      No     No     No    Yes
## 5      No     Yes     Yes     Yes   Nope     Yes     No     No    Yes
## 6                                                                    
##   Q98869 Q98578     Q98059 Q98078 Q98197 Q96024
## 1     No        Only-child     No     No    Yes
## 2     No     No Only-child    Yes     No     No
## 3    Yes     No        Yes     No    Yes     No
## 4    Yes     No        Yes     No     No    Yes
## 5     No     No        Yes     No     No    Yes
## 6                                              
##      USER_ID  YOB Gender              Income             HouseholdStatus
## 193      245 1964   Male       over $150,000            Married (w/kids)
## 848     1046 1953   Male $100,001 - $150,000 Domestic Partners (no kids)
## 2836    3530 1995   Male                                Single (no kids)
## 4052    5050 1945 Female  $75,000 - $100,000            Married (w/kids)
## 4093    5107 1980 Female $100,001 - $150,000            Married (w/kids)
## 5509    6888 1998 Female       under $25,000            Single (no kids)
##             EducationLevel      Party Q124742 Q124122 Q123464 Q123621
## 193      Bachelor's Degree Republican     Yes     Yes      No     Yes
## 848                          Democrat                                
## 2836 Current Undergraduate   Democrat     Yes     Yes     Yes      No
## 4052     Bachelor's Degree Republican                                
## 4093     Bachelor's Degree   Democrat                      No      No
## 5509          Current K-12 Republican                                
##      Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 193       No     Yes  Public      No     Yes      No     Yes      No
## 848                                                                 
## 2836             Yes  Public     Yes      No      No     Yes     Yes
## 4052              No  Public                                        
## 4093      No      No Private      No                                
## 5509                                                     Yes     Yes
##      Q120379 Q120650 Q120472     Q120194 Q120012 Q120014 Q119334 Q119851
## 193       No     Yes Science   Try first     Yes     Yes     Yes      No
## 848                                                                     
## 2836     Yes     Yes     Art Study first      No     Yes             Yes
## 4052                                                                    
## 4093                                                         Yes        
## 5509     Yes      No     Art Study first     Yes      No     Yes      No
##      Q119650 Q118892 Q118117    Q118232 Q118233 Q118237     Q117186
## 193   Giving     Yes      No   Idealist     Yes     Yes  Hot headed
## 848                                                                
## 2836             Yes     Yes   Idealist     Yes      No Cool headed
## 4052                      No                 No      No            
## 4093              No      No Pragmatist      No     Yes            
## 5509  Giving      No                                               
##             Q117193 Q116797 Q116881 Q116953 Q116601 Q116441 Q116448
## 193  Standard hours      No   Happy     Yes     Yes      No      No
## 848                                                                
## 2836      Odd hours      No   Happy     Yes     Yes              No
## 4052                                                               
## 4093                                                               
## 5509                                                               
##      Q116197 Q115602 Q115777 Q115610 Q115611       Q115899 Q115390 Q114961
## 193     A.M.     Yes     End     Yes     Yes            Me      No      No
## 848                                                                       
## 2836             Yes     End     Yes      No Circumstances     Yes      No
## 4052    P.M.     Yes   Start     Yes      No                    No        
## 4093    P.M.     Yes   Start     Yes      No Circumstances                
## 5509                                                                      
##      Q114748 Q115195 Q114517    Q114386 Q113992 Q114152 Q113583    Q113584
## 193      Yes      No     Yes        TMI      No     Yes   Tunes Technology
## 848                                                                       
## 2836     Yes      No      No Mysterious      No     Yes   Tunes     People
## 4052      No     Yes                                                      
## 4093                                                      Tunes     People
## 5509                                                                      
##      Q113181 Q112478 Q112512 Q112270 Q111848    Q111580 Q111220 Q110740
## 193       No     Yes             Yes     Yes Supportive      No     Mac
## 848                                                                    
## 2836     Yes     Yes     Yes      No     Yes  Demanding     Yes      PC
## 4052                                                                   
## 4093                                     Yes Supportive                
## 5509                                                                   
##      Q109367       Q108950 Q109244 Q108855 Q108617   Q108856 Q108754
## 193       No      Cautious      No    Yes!      No Socialize      No
## 848      Yes Risk-friendly     Yes    Yes!      No     Space      No
## 2836     Yes      Cautious     Yes             Yes                  
## 4052                                                                
## 4093      No Risk-friendly      No    Yes!      No     Space      No
## 5509                                                                
##        Q108342 Q108343 Q107869 Q107491 Q106993     Q106997 Q106272 Q106388
## 193  In-person      No     Yes     Yes      No Yay people!     Yes     Yes
## 848  In-person     Yes                                                    
## 2836 In-person     Yes             Yes                         Yes      No
## 4052                                        No Grrr people                
## 4093 In-person     Yes     Yes     Yes     Yes Yay people!     Yes     Yes
## 5509                                                                      
##      Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906 Q102674
## 193       No     Yes      No      No     Yes      No      No      No
## 848                                                                 
## 2836     Yes      No      No      No     Yes     Yes      No      No
## 4052                              No      No      No              No
## 4093      No      No      No      No     Yes      No      No     Yes
## 5509                                                                
##      Q102687 Q102289 Q102089  Q101162 Q101163 Q101596 Q100689 Q100680
## 193       No      No     Own Optimist     Dad     Yes     Yes      No
## 848                                                                  
## 2836     Yes     Yes    Rent Optimist     Dad      No     Yes     Yes
## 4052     Yes             Own                       No                
## 4093     Yes     Yes    Rent                               No     Yes
## 5509                                                                 
##      Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 193      Yes Check!      No     No     No    Yes    Yes     No    Yes
## 848                                                                  
## 2836     Yes Check!      No     No     No    Yes    Yes           Yes
## 4052                                                                 
## 4093      No   Nope     Yes     No    Yes    Yes    Yes     No    Yes
## 5509                                                                 
##      Q98078 Q98197 Q96024
## 193      No    Yes    Yes
## 848                    No
## 2836    Yes    Yes     No
## 4052                     
## 4093    Yes    Yes     No
## 5509                     
##      USER_ID  YOB Gender            Income  HouseholdStatus
## 5563    6955 1966   Male     over $150,000 Married (w/kids)
## 5564    6956   NA   Male                                   
## 5565    6957 2000 Female                                   
## 5566    6958 1969   Male     over $150,000                 
## 5567    6959 1986   Male $25,001 - $50,000 Married (w/kids)
## 5568    6960 1999   Male     under $25,000 Single (no kids)
##           EducationLevel      Party Q124742 Q124122 Q123464 Q123621
## 5563   Bachelor's Degree   Democrat                                
## 5564     Master's Degree   Democrat              No      No        
## 5565        Current K-12 Republican                                
## 5566   Bachelor's Degree   Democrat                             Yes
## 5567 High School Diploma Republican                                
## 5568        Current K-12 Republican                                
##      Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 5563                              No     Yes      No     Yes     Yes
## 5564      No     Yes  Public             Yes                        
## 5565                  Public                             Yes        
## 5566                              No      No      No     Yes     Yes
## 5567                             Yes             Yes              No
## 5568                                     Yes      No      No        
##      Q120379 Q120650 Q120472   Q120194 Q120012 Q120014 Q119334 Q119851
## 5563                                                                  
## 5564                                                                  
## 5565     Yes     Yes     Art Try first      No     Yes     Yes     Yes
## 5566     Yes     Yes Science                                          
## 5567      No      No Science                No     Yes                
## 5568                                                                  
##        Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186 Q117193
## 5563                                                                  
## 5564                                                                  
## 5565 Receiving                                                        
## 5566                                                                  
## 5567                                                                  
## 5568                                                                  
##      Q116797 Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q115777 Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q114517 Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q112512 Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716
## 5563                                                                      
## 5564                                                                      
## 5565                                                                      
## 5566                                                                      
## 5567                                                                      
## 5568                                                                      
##      Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 5563                                                        
## 5564                                                        
## 5565                                                        
## 5566                                                        
## 5567                                                        
## 5568                                                        
## 'data.frame':    5568 obs. of  20 variables:
##  $ USER_ID        : int  1 4 5 8 9 10 11 12 13 15 ...
##  $ YOB            : int  1938 1970 1997 1983 1984 1997 1983 1996 NA 1981 ...
##  $ Gender         : chr  "Male" "Female" "Male" "Male" ...
##  $ Income         : chr  "" "over $150,000" "$75,000 - $100,000" "$100,001 - $150,000" ...
##  $ HouseholdStatus: chr  "Married (w/kids)" "Domestic Partners (w/kids)" "Single (no kids)" "Married (w/kids)" ...
##  $ EducationLevel : chr  "" "Bachelor's Degree" "High School Diploma" "Bachelor's Degree" ...
##  $ Party          : chr  "Democrat" "Democrat" "Republican" "Democrat" ...
##  $ Q124742        : chr  "No" "" "" "No" ...
##  $ Q124122        : chr  "" "Yes" "Yes" "Yes" ...
##  $ Q123464        : chr  "No" "No" "Yes" "No" ...
##  $ Q123621        : chr  "No" "No" "No" "Yes" ...
##  $ Q122769        : chr  "No" "No" "" "No" ...
##  $ Q122770        : chr  "Yes" "Yes" "Yes" "No" ...
##  $ Q122771        : chr  "Public" "Public" "Private" "Public" ...
##  $ Q122120        : chr  "No" "No" "No" "No" ...
##  $ Q121699        : chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q121700        : chr  "No" "No" "No" "No" ...
##  $ Q120978        : chr  "" "Yes" "Yes" "Yes" ...
##  $ Q121011        : chr  "No" "No" "No" "No" ...
##  $ Q120379        : chr  "No" "No" "No" "No" ...
## NULL
## 'data.frame':    5568 obs. of  20 variables:
##  $ Q120650: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q118117: chr  "Yes" "No" "Yes" "No" ...
##  $ Q118233: chr  "No" "No" "No" "No" ...
##  $ Q118237: chr  "No" "No" "Yes" "No" ...
##  $ Q116441: chr  "No" "Yes" "No" "No" ...
##  $ Q116197: chr  "P.M." "A.M." "A.M." "A.M." ...
##  $ Q115611: chr  "No" "No" "Yes" "No" ...
##  $ Q115899: chr  "Circumstances" "Me" "Circumstances" "Circumstances" ...
##  $ Q115390: chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q114748: chr  "Yes" "No" "No" "No" ...
##  $ Q115195: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q113584: chr  "Technology" "" "Technology" "People" ...
##  $ Q112478: chr  "No" "" "Yes" "Yes" ...
##  $ Q112270: chr  "" "" "Yes" "Yes" ...
##  $ Q111848: chr  "No" "" "No" "Yes" ...
##  $ Q106993: chr  "Yes" "No" "Yes" "Yes" ...
##  $ Q106388: chr  "No" "Yes" "No" "No" ...
##  $ Q105655: chr  "No" "No" "No" "Yes" ...
##  $ Q104996: chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q102674: chr  "No" "" "No" "No" ...
## NULL
## 'data.frame':    5568 obs. of  21 variables:
##  $ Q102674: chr  "No" "" "No" "No" ...
##  $ Q102687: chr  "Yes" "" "Yes" "Yes" ...
##  $ Q102289: chr  "No" "" "No" "Yes" ...
##  $ Q102089: chr  "Own" "" "Own" "Own" ...
##  $ Q101162: chr  "Optimist" "" "Pessimist" "Optimist" ...
##  $ Q101163: chr  "" "" "Mom" "Mom" ...
##  $ Q101596: chr  "Yes" "" "No" "No" ...
##  $ Q100689: chr  "Yes" "" "No" "No" ...
##  $ Q100680: chr  "No" "" "No" "No" ...
##  $ Q100562: chr  "No" "" "No" "Yes" ...
##  $ Q99982 : chr  "Nope" "" "Nope" "Check!" ...
##  $ Q100010: chr  "Yes" "" "Yes" "No" ...
##  $ Q99716 : chr  "No" "" "No" "No" ...
##  $ Q99581 : chr  "No" "" "No" "No" ...
##  $ Q99480 : chr  "" "No" "No" "Yes" ...
##  $ Q98869 : chr  "No" "No" "Yes" "Yes" ...
##  $ Q98578 : chr  "" "No" "No" "No" ...
##  $ Q98059 : chr  "Only-child" "Only-child" "Yes" "Yes" ...
##  $ Q98078 : chr  "No" "Yes" "No" "No" ...
##  $ Q98197 : chr  "No" "No" "Yes" "No" ...
##  $ Q96024 : chr  "Yes" "No" "No" "Yes" ...
## NULL
## Warning in myprint_str_df(obsDf): [list output truncated]
## [1] "Reading file ./data/test2016.csv..."
## [1] "dimensions of data in ./data/test2016.csv: 1,392 rows x 107 cols"
##   USER_ID  YOB Gender             Income   HouseholdStatus
## 1       2 1985 Female  $25,001 - $50,000  Single (no kids)
## 2       3 1983   Male  $50,000 - $74,999  Married (w/kids)
## 3       6 1995   Male $75,000 - $100,000  Single (no kids)
## 4       7 1980 Female  $50,000 - $74,999  Single (no kids)
## 5      14 1980 Female                    Married (no kids)
## 6      28 1973   Male      over $150,000 Married (no kids)
##          EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1       Master's Degree             Yes      No     Yes      No      No
## 2 Current Undergraduate                      No             Yes     Yes
## 3          Current K-12                                                
## 4       Master's Degree     Yes     Yes      No     Yes     Yes     Yes
## 5 Current Undergraduate             Yes      No     Yes      No      No
## 6       Master's Degree      No     Yes      No     Yes      No      No
##   Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650 Q120472
## 1  Public      No     Yes     Yes     Yes      No     Yes     Yes Science
## 2  Public      No     Yes      No                                        
## 3                      No      No      No     Yes      No     Yes Science
## 4  Public      No     Yes      No     Yes      No     Yes     Yes Science
## 5  Public     Yes     Yes      No     Yes     Yes      No     Yes     Art
## 6  Public      No     Yes      No     Yes     Yes     Yes     Yes Science
##       Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892 Q118117
## 1 Study first     Yes     Yes     Yes      No  Giving     Yes      No
## 2 Study first      No     Yes              No                        
## 3   Try first      No     Yes      No     Yes  Giving                
## 4   Try first     Yes      No      No     Yes  Giving     Yes     Yes
## 5   Try first     Yes     Yes     Yes     Yes  Giving      No      No
## 6   Try first     Yes     Yes      No      No  Giving      No     Yes
##      Q118232 Q118233 Q118237     Q117186        Q117193 Q116797 Q116881
## 1   Idealist      No     Yes Cool headed      Odd hours     Yes   Happy
## 2                                                                      
## 3                                                                      
## 4   Idealist      No      No Cool headed Standard hours      No   Happy
## 5   Idealist      No     Yes  Hot headed Standard hours     Yes   Happy
## 6 Pragmatist     Yes      No  Hot headed      Odd hours     Yes   Right
##   Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610 Q115611
## 1     Yes     Yes      No     Yes    A.M.     Yes     End     Yes      No
## 2     Yes     Yes                    P.M.                                
## 3     Yes                                                                
## 4     Yes      No      No     Yes    A.M.     Yes   Start     Yes      No
## 5     Yes     Yes     Yes      No    P.M.     Yes     End      No      No
## 6     Yes     Yes     Yes     Yes    P.M.             End     Yes     Yes
##         Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386 Q113992
## 1            Me      No     Yes      No     Yes     Yes     TMI        
## 2                                            No                     Yes
## 3                   Yes      No     Yes     Yes      No     TMI      No
## 4            Me     Yes      No     Yes     Yes     Yes     TMI      No
## 5            Me      No      No      No     Yes      No     TMI      No
## 6 Circumstances      No     Yes      No     Yes      No     TMI     Yes
##   Q114152 Q113583    Q113584 Q113181 Q112478 Q112512 Q112270 Q111848
## 1      No   Tunes     People     Yes     Yes      No     Yes     Yes
## 2      No                         No                      No     Yes
## 3      No   Tunes Technology     Yes      No     Yes      No        
## 4     Yes    Talk     People      No      No     Yes      No     Yes
## 5           Tunes Technology      No     Yes     Yes             Yes
## 6      No    Talk Technology      No     Yes     Yes      No     Yes
##      Q111580 Q111220 Q110740 Q109367  Q108950 Q109244 Q108855 Q108617
## 1 Supportive      No             Yes Cautious     Yes    Yes!        
## 2                 No             Yes Cautious      No    Yes!      No
## 3                                 No               No              No
## 4 Supportive      No      PC      No Cautious     Yes    Yes!      No
## 5 Supportive     Yes     Mac     Yes Cautious      No    Yes!      No
## 6  Demanding      No      PC     Yes Cautious      No  Umm...      No
##   Q108856 Q108754   Q108342 Q108343 Q107869 Q107491 Q106993     Q106997
## 1             Yes In-person     Yes                                    
## 2   Space      No                       Yes     Yes     Yes Grrr people
## 3             Yes In-person      No      No     Yes     Yes Yay people!
## 4   Space      No    Online      No      No     Yes     Yes Yay people!
## 5   Space      No In-person      No      No     Yes      No Grrr people
## 6   Space      No In-person     Yes             Yes     Yes Grrr people
##   Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906
## 1                                                                        
## 2     Yes      No      No     Yes      No     Yes      No      No        
## 3     Yes      No     Yes      No      No     Yes     Yes      No      No
## 4      No      No      No      No      No     Yes     Yes      No      No
## 5      No      No      No     Yes     Yes     Yes     Yes     Yes      No
## 6     Yes      No     Yes     Yes      No      No      No     Yes     Yes
##   Q102674 Q102687 Q102289 Q102089   Q101162 Q101163 Q101596 Q100689
## 1                                                                No
## 2                            Rent Pessimist     Dad                
## 3      No      No     Yes     Own  Optimist     Mom      No      No
## 4      No      No      No     Own  Optimist     Dad      No      No
## 5     Yes      No      No     Own Pessimist     Mom      No     Yes
## 6     Yes     Yes      No     Own Pessimist     Mom      No     Yes
##   Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 1     Yes     Yes                                        Yes              
## 2             Yes                                        Yes           Yes
## 3     Yes     Yes   Nope      No     No     No    Yes    Yes     No    Yes
## 4     Yes     Yes   Nope     Yes     No     No     No    Yes     No    Yes
## 5     Yes     Yes   Nope     Yes     No     No    Yes     No     No    Yes
## 6     Yes     Yes   Nope     Yes     No     No    Yes     No     No    Yes
##   Q98078 Q98197 Q96024
## 1                     
## 2    Yes     No    Yes
## 3     No    Yes    Yes
## 4     No     No    Yes
## 5     No     No     No
## 6     No     No    Yes
##      USER_ID  YOB Gender              Income   HouseholdStatus
## 503     2555 1956   Male       over $150,000  Married (w/kids)
## 515     2616 1959   Male       over $150,000  Married (w/kids)
## 857     4346 1990 Female   $50,000 - $74,999                  
## 950     4814 1969   Male  $75,000 - $100,000  Married (w/kids)
## 1207    6057 1937 Female   $25,001 - $50,000 Married (no kids)
## 1255    6285 1976 Female $100,001 - $150,000 Married (no kids)
##         EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 503  Bachelor's Degree      No      No      No     Yes      No     Yes
## 515  Bachelor's Degree                                                
## 857  Bachelor's Degree                                                
## 950  Bachelor's Degree             Yes      No     Yes      No      No
## 1207 Bachelor's Degree                                      No     Yes
## 1255 Bachelor's Degree                                                
##      Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 503  Private      No     Yes      No      No     Yes      No     Yes
## 515               No      No                                        
## 857               No     Yes      No      No      No      No     Yes
## 950   Public     Yes     Yes      No     Yes     Yes      No     Yes
## 1207  Public      No     Yes      No      No      No              No
## 1255                                                                
##      Q120472     Q120194 Q120012 Q120014 Q119334 Q119851   Q119650 Q118892
## 503  Science Study first      No     Yes      No     Yes    Giving     Yes
## 515                                                                    Yes
## 857  Science Study first      No      No     Yes      No Receiving     Yes
## 950  Science Study first      No      No      No      No    Giving      No
## 1207         Study first      No      No             Yes Receiving     Yes
## 1255                                                                      
##      Q118117    Q118232 Q118233 Q118237     Q117186        Q117193 Q116797
## 503       No Pragmatist      No      No Cool headed Standard hours      No
## 515       No Pragmatist      No     Yes Cool headed Standard hours      No
## 857      Yes Pragmatist      No      No Cool headed      Odd hours      No
## 950       No Pragmatist      No     Yes  Hot headed      Odd hours     Yes
## 1207      No Pragmatist      No      No  Hot headed                     No
## 1255                                                                      
##      Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777
## 503    Happy     Yes     Yes      No      No    A.M.     Yes     End
## 515    Right     Yes     Yes      No     Yes             Yes        
## 857    Right     Yes     Yes      No      No    A.M.     Yes   Start
## 950    Happy     Yes     Yes     Yes      No    P.M.     Yes   Start
## 1207   Happy     Yes     Yes      No      No    A.M.     Yes   Start
## 1255                     Yes      No     Yes    A.M.     Yes   Start
##      Q115610 Q115611       Q115899 Q115390 Q114961 Q114748 Q115195 Q114517
## 503      Yes     Yes            Me      No      No      No     Yes     Yes
## 515      Yes      No            Me     Yes      No     Yes     Yes      No
## 857      Yes      No            Me              No      No      No     Yes
## 950      Yes      No            Me     Yes      No     Yes      No      No
## 1207      No      No Circumstances     Yes      No     Yes      No     Yes
## 1255     Yes      No Circumstances      No     Yes      No     Yes     Yes
##         Q114386 Q113992 Q114152 Q113583    Q113584 Q113181 Q112478 Q112512
## 503         TMI     Yes     Yes   Tunes     People     Yes      No     Yes
## 515                  No     Yes    Talk Technology                        
## 857  Mysterious      No      No   Tunes     People      No      No      No
## 950  Mysterious      No      No   Tunes     People     Yes     Yes     Yes
## 1207                Yes      No    Talk                                Yes
## 1255        TMI             Yes                                Yes     Yes
##      Q112270 Q111848    Q111580 Q111220 Q110740 Q109367       Q108950
## 503       No     Yes  Demanding      No      PC      No      Cautious
## 515       No     Yes                 No     Mac     Yes              
## 857      Yes     Yes Supportive      No     Mac      No Risk-friendly
## 950       No     Yes Supportive     Yes      PC      No      Cautious
## 1207                 Supportive      No      PC              Cautious
## 1255     Yes     Yes  Demanding      No     Mac                      
##      Q109244 Q108855 Q108617 Q108856 Q108754   Q108342 Q108343 Q107869
## 503       No  Umm...      No   Space      No In-person      No     Yes
## 515                                                                   
## 857      Yes  Umm...      No   Space      No In-person      No     Yes
## 950       No    Yes!      No   Space      No In-person      No      No
## 1207            Yes!      No   Space      No In-person      No     Yes
## 1255                                                                  
##      Q107491 Q106993     Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 503      Yes     Yes Yay people!     Yes      No      No     Yes      No
## 515                                                                   No
## 857       No     Yes Grrr people     Yes      No     Yes      No      No
## 950      Yes      No Grrr people     Yes     Yes      No      No      No
## 1207     Yes     Yes                 Yes                                
## 1255                                                                    
##      Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 503       No     Yes      No      No      No     Yes      No     Own
## 515      Yes     Yes                                                
## 857       No     Yes     Yes      No      No     Yes     Yes     Own
## 950      Yes     Yes     Yes      No      No     Yes      No     Own
## 1207     Yes                                                        
## 1255                                                                
##        Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010
## 503  Pessimist     Mom     Yes     Yes      No     Yes Check!     Yes
## 515                                                    Check!     Yes
## 857   Optimist     Mom      No     Yes     Yes      No   Nope     Yes
## 950  Pessimist     Mom     Yes      No      No      No Check!     Yes
## 1207                                                                 
## 1255                                                                 
##      Q99716 Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 503      No     No    Yes    Yes     No    Yes    Yes    Yes    Yes
## 515             No    Yes    Yes           Yes     No    Yes    Yes
## 857      No    Yes    Yes    Yes     No    Yes     No     No     No
## 950      No     No    Yes    Yes     No    Yes     No    Yes    Yes
## 1207                                                               
## 1255                                                               
##      USER_ID  YOB Gender              Income             HouseholdStatus
## 1387    6922 1988   Male   $50,000 - $74,999            Single (no kids)
## 1388    6928 1977 Female   $50,000 - $74,999 Domestic Partners (no kids)
## 1389    6930 1998 Female $100,001 - $150,000            Single (no kids)
## 1390    6941 1989   Male   $25,001 - $50,000           Married (no kids)
## 1391    6946 1996   Male                                                
## 1392    6947   NA Female                                                
##         EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1387   Master's Degree                                                
## 1388   Master's Degree                                                
## 1389      Current K-12                                      No      No
## 1390 Bachelor's Degree                                                
## 1391      Current K-12                                                
## 1392                       Yes     Yes      No      No      No      No
##      Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1387                     Yes     Yes     Yes     Yes     Yes     Yes
## 1388                             Yes              No             Yes
## 1389  Public     Yes     Yes     Yes     Yes     Yes     Yes     Yes
## 1390             Yes     Yes      No      No      No                
## 1391             Yes      No      No     Yes      No     Yes     Yes
## 1392  Public     Yes     Yes      No     Yes     Yes     Yes     Yes
##      Q120472     Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1387 Science   Try first      No     Yes     Yes      No  Giving        
## 1388     Art                                                            
## 1389     Art Study first     Yes      No     Yes      No  Giving        
## 1390                                                                    
## 1391     Art Study first     Yes     Yes     Yes      No  Giving        
## 1392     Art                  No      No      No     Yes  Giving        
##      Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1387                                                                    
## 1388                                                                    
## 1389                                                                    
## 1390                                                                    
## 1391                                                                    
## 1392                                                                    
##      Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1387                                          
## 1388                                          
## 1389                                          
## 1390                                          
## 1391                                          
## 1392                                          
## 'data.frame':    1392 obs. of  20 variables:
##  $ USER_ID        : int  2 3 6 7 14 28 29 37 44 56 ...
##  $ YOB            : int  1985 1983 1995 1980 1980 1973 1968 1961 1989 1975 ...
##  $ Gender         : chr  "Female" "Male" "Male" "Female" ...
##  $ Income         : chr  "$25,001 - $50,000" "$50,000 - $74,999" "$75,000 - $100,000" "$50,000 - $74,999" ...
##  $ HouseholdStatus: chr  "Single (no kids)" "Married (w/kids)" "Single (no kids)" "Single (no kids)" ...
##  $ EducationLevel : chr  "Master's Degree" "Current Undergraduate" "Current K-12" "Master's Degree" ...
##  $ Q124742        : chr  "" "" "" "Yes" ...
##  $ Q124122        : chr  "Yes" "" "" "Yes" ...
##  $ Q123464        : chr  "No" "No" "" "No" ...
##  $ Q123621        : chr  "Yes" "" "" "Yes" ...
##  $ Q122769        : chr  "No" "Yes" "" "Yes" ...
##  $ Q122770        : chr  "No" "Yes" "" "Yes" ...
##  $ Q122771        : chr  "Public" "Public" "" "Public" ...
##  $ Q122120        : chr  "No" "No" "" "No" ...
##  $ Q121699        : chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q121700        : chr  "Yes" "No" "No" "No" ...
##  $ Q120978        : chr  "Yes" "" "No" "Yes" ...
##  $ Q121011        : chr  "No" "" "Yes" "No" ...
##  $ Q120379        : chr  "Yes" "" "No" "Yes" ...
##  $ Q120650        : chr  "Yes" "" "Yes" "Yes" ...
## NULL
## 'data.frame':    1392 obs. of  20 variables:
##  $ Q120012: chr  "Yes" "No" "No" "Yes" ...
##  $ Q120014: chr  "Yes" "Yes" "Yes" "No" ...
##  $ Q118117: chr  "No" "" "" "Yes" ...
##  $ Q118237: chr  "Yes" "" "" "No" ...
##  $ Q116953: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q116601: chr  "Yes" "Yes" "" "No" ...
##  $ Q116448: chr  "Yes" "" "" "Yes" ...
##  $ Q116197: chr  "A.M." "P.M." "" "A.M." ...
##  $ Q115899: chr  "Me" "" "" "Me" ...
##  $ Q114961: chr  "Yes" "" "No" "No" ...
##  $ Q113584: chr  "People" "" "Technology" "People" ...
##  $ Q113181: chr  "Yes" "No" "Yes" "No" ...
##  $ Q112512: chr  "No" "" "Yes" "Yes" ...
##  $ Q108950: chr  "Cautious" "Cautious" "" "Cautious" ...
##  $ Q108617: chr  "" "No" "No" "No" ...
##  $ Q108342: chr  "In-person" "" "In-person" "Online" ...
##  $ Q107491: chr  "" "Yes" "Yes" "Yes" ...
##  $ Q106272: chr  "" "Yes" "Yes" "No" ...
##  $ Q106389: chr  "" "No" "Yes" "No" ...
##  $ Q104996: chr  "" "No" "Yes" "Yes" ...
## NULL
## 'data.frame':    1392 obs. of  21 variables:
##  $ Q102674: chr  "" "" "No" "No" ...
##  $ Q102687: chr  "" "" "No" "No" ...
##  $ Q102289: chr  "" "" "Yes" "No" ...
##  $ Q102089: chr  "" "Rent" "Own" "Own" ...
##  $ Q101162: chr  "" "Pessimist" "Optimist" "Optimist" ...
##  $ Q101163: chr  "" "Dad" "Mom" "Dad" ...
##  $ Q101596: chr  "" "" "No" "No" ...
##  $ Q100689: chr  "No" "" "No" "No" ...
##  $ Q100680: chr  "Yes" "" "Yes" "Yes" ...
##  $ Q100562: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q99982 : chr  "" "" "Nope" "Nope" ...
##  $ Q100010: chr  "" "" "No" "Yes" ...
##  $ Q99716 : chr  "" "" "No" "No" ...
##  $ Q99581 : chr  "" "" "No" "No" ...
##  $ Q99480 : chr  "" "" "Yes" "No" ...
##  $ Q98869 : chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q98578 : chr  "" "" "No" "No" ...
##  $ Q98059 : chr  "" "Yes" "Yes" "Yes" ...
##  $ Q98078 : chr  "" "Yes" "No" "No" ...
##  $ Q98197 : chr  "" "No" "Yes" "No" ...
##  $ Q96024 : chr  "" "Yes" "Yes" "Yes" ...
## NULL
## Warning in myprint_str_df(obsDf): [list output truncated]
## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: YOB.Age.fctr..."
## [1] "Creating new feature: Gender.fctr..."
## [1] "Creating new feature: Income.fctr..."
## [1] "Creating new feature: Hhold.fctr..."
## [1] "Creating new feature: Edn.fctr..."
## [1] "Creating new feature: Q124742.fctr..."
## [1] "Creating new feature: Q124122.fctr..."
## [1] "Creating new feature: Q123621.fctr..."
## [1] "Creating new feature: Q123464.fctr..."
## [1] "Creating new feature: Q122771.fctr..."
## [1] "Creating new feature: Q122770.fctr..."
## [1] "Creating new feature: Q122769.fctr..."
## [1] "Creating new feature: Q122120.fctr..."
## [1] "Creating new feature: Q121700.fctr..."
## [1] "Creating new feature: Q121699.fctr..."
## [1] "Creating new feature: Q121011.fctr..."
## [1] "Creating new feature: Q120978.fctr..."
## [1] "Creating new feature: Q120650.fctr..."
## [1] "Creating new feature: Q120472.fctr..."
## [1] "Creating new feature: Q120379.fctr..."
## [1] "Creating new feature: Q120194.fctr..."
## [1] "Creating new feature: Q120014.fctr..."
## [1] "Creating new feature: Q120012.fctr..."
## [1] "Creating new feature: Q119851.fctr..."
## [1] "Creating new feature: Q119650.fctr..."
## [1] "Creating new feature: Q119334.fctr..."
## [1] "Creating new feature: Q118892.fctr..."
## [1] "Creating new feature: Q118237.fctr..."
## [1] "Creating new feature: Q118233.fctr..."
## [1] "Creating new feature: Q118232.fctr..."
## [1] "Creating new feature: Q118117.fctr..."
## [1] "Creating new feature: Q117193.fctr..."
## [1] "Creating new feature: Q117186.fctr..."
## [1] "Creating new feature: Q116797.fctr..."
## [1] "Creating new feature: Q116881.fctr..."
## [1] "Creating new feature: Q116953.fctr..."
## [1] "Creating new feature: Q116601.fctr..."
## [1] "Creating new feature: Q116441.fctr..."
## [1] "Creating new feature: Q116448.fctr..."
## [1] "Creating new feature: Q116197.fctr..."
## [1] "Creating new feature: Q115602.fctr..."
## [1] "Creating new feature: Q115777.fctr..."
## [1] "Creating new feature: Q115610.fctr..."
## [1] "Creating new feature: Q115611.fctr..."
## [1] "Creating new feature: Q115899.fctr..."
## [1] "Creating new feature: Q115390.fctr..."
## [1] "Creating new feature: Q115195.fctr..."
## [1] "Creating new feature: Q114961.fctr..."
## [1] "Creating new feature: Q114748.fctr..."
## [1] "Creating new feature: Q114517.fctr..."
## [1] "Creating new feature: Q114386.fctr..."
## [1] "Creating new feature: Q114152.fctr..."
## [1] "Creating new feature: Q113992.fctr..."
## [1] "Creating new feature: Q113583.fctr..."
## [1] "Creating new feature: Q113584.fctr..."
## [1] "Creating new feature: Q113181.fctr..."
## [1] "Creating new feature: Q112478.fctr..."
## [1] "Creating new feature: Q112512.fctr..."
## [1] "Creating new feature: Q112270.fctr..."
## [1] "Creating new feature: Q111848.fctr..."
## [1] "Creating new feature: Q111580.fctr..."
## [1] "Creating new feature: Q111220.fctr..."
## [1] "Creating new feature: Q110740.fctr..."
## [1] "Creating new feature: Q109367.fctr..."
## [1] "Creating new feature: Q109244.fctr..."
## [1] "Creating new feature: Q108950.fctr..."
## [1] "Creating new feature: Q108855.fctr..."
## [1] "Creating new feature: Q108617.fctr..."
## [1] "Creating new feature: Q108856.fctr..."
## [1] "Creating new feature: Q108754.fctr..."
## [1] "Creating new feature: Q108342.fctr..."
## [1] "Creating new feature: Q108343.fctr..."
## [1] "Creating new feature: Q107869.fctr..."
## [1] "Creating new feature: Q107491.fctr..."
## [1] "Creating new feature: Q106993.fctr..."
## [1] "Creating new feature: Q106997.fctr..."
## [1] "Creating new feature: Q106272.fctr..."
## [1] "Creating new feature: Q106388.fctr..."
## [1] "Creating new feature: Q106389.fctr..."
## [1] "Creating new feature: Q106042.fctr..."
## [1] "Creating new feature: Q105840.fctr..."
## [1] "Creating new feature: Q105655.fctr..."
## [1] "Creating new feature: Q104996.fctr..."
## [1] "Creating new feature: Q103293.fctr..."
## [1] "Creating new feature: Q102906.fctr..."
## [1] "Creating new feature: Q102674.fctr..."
## [1] "Creating new feature: Q102687.fctr..."
## [1] "Creating new feature: Q102289.fctr..."
## [1] "Creating new feature: Q102089.fctr..."
## [1] "Creating new feature: Q101162.fctr..."
## [1] "Creating new feature: Q101163.fctr..."
## [1] "Creating new feature: Q101596.fctr..."
## [1] "Creating new feature: Q100689.fctr..."
## [1] "Creating new feature: Q100680.fctr..."
## [1] "Creating new feature: Q100562.fctr..."
## [1] "Creating new feature: Q100010.fctr..."
## [1] "Partition stats:"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
##        Party  .src   .n
## 1   Democrat Train 2951
## 2 Republican Train 2617
## 3       <NA>  Test 1392
##        Party  .src   .n
## 1   Democrat Train 2951
## 2 Republican Train 2617
## 3       <NA>  Test 1392
## Loading required package: RColorBrewer

##    .src   .n
## 1 Train 5568
## 2  Test 1392
## Loading required package: lazyeval
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
## [1] "Found 0 duplicates by all features:"
## NULL
##          label step_major step_minor label_minor    bgn    end elapsed
## 1  import.data          1          0           0 10.210 24.134  13.924
## 2 inspect.data          2          0           0 24.134     NA      NA

Step 2.0: inspect data

## Warning: Removed 1392 rows containing non-finite values (stat_count).
## Loading required package: reshape2

##       Party.Democrat Party.Republican Party.NA
## Test              NA               NA     1392
## Train           2951             2617       NA
##       Party.Democrat Party.Republican Party.NA
## Test              NA               NA        1
## Train      0.5299928        0.4700072       NA
## [1] "numeric data missing in glbObsAll: "
## YOB 
## 415 
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##          Gender          Income HouseholdStatus  EducationLevel 
##             143            1273             552            1067 
##           Party         Q124742         Q124122         Q123464 
##              NA            4340            3114            2912 
##         Q123621         Q122769         Q122770         Q122771 
##            3018            2778            2597            2579 
##         Q122120         Q121699         Q121700         Q120978 
##            2552            2279            2328            2303 
##         Q121011         Q120379         Q120650         Q120472 
##            2256            2361            2283            2433 
##         Q120194         Q120012         Q120014         Q119334 
##            2603            2344            2571            2477 
##         Q119851         Q119650         Q118892         Q118117 
##            2243            2374            2206            2342 
##         Q118232         Q118233         Q118237         Q117186 
##            3018            2659            2592            2845 
##         Q117193         Q116797         Q116881         Q116953 
##            2799            2771            2889            2848 
##         Q116601         Q116441         Q116448         Q116197 
##            2606            2684            2730            2657 
##         Q115602         Q115777         Q115610         Q115611 
##            2619            2785            2637            2443 
##         Q115899         Q115390         Q114961         Q114748 
##            2789            2860            2687            2462 
##         Q115195         Q114517         Q114386         Q113992 
##            2647            2567            2686            2502 
##         Q114152         Q113583         Q113584         Q113181 
##            2829            2632            2654            2576 
##         Q112478         Q112512         Q112270         Q111848 
##            2790            2676            2820            2449 
##         Q111580         Q111220         Q110740         Q109367 
##            2686            2563            2479            2624 
##         Q108950         Q109244         Q108855         Q108617 
##            2641            2731            3008            2696 
##         Q108856         Q108754         Q108342         Q108343 
##            3007            2770            2760            2736 
##         Q107869         Q107491         Q106993         Q106997 
##            2762            2667            2676            2702 
##         Q106272         Q106388         Q106389         Q106042 
##            2722            2818            2871            2762 
##         Q105840         Q105655         Q104996         Q103293 
##            2876            2612            2620            2674 
##         Q102906         Q102674         Q102687         Q102289 
##            2840            2864            2712            2790 
##         Q102089         Q101162         Q101163         Q101596 
##            2736            2816            2995            2824 
##         Q100689         Q100680         Q100562          Q99982 
##            2568            2787            2793            2871 
##         Q100010          Q99716          Q99581          Q99480 
##            2688            2790            2690            2700 
##          Q98869          Q98578          Q98059          Q98078 
##            2906            2867            2629            2945 
##          Q98197          Q96024 
##            2836            2858
##        Party Party.fctr   .n
## 1   Democrat          D 2951
## 2 Republican          R 2617
## 3       <NA>       <NA> 1392
## Warning: Removed 1 rows containing missing values (position_stack).

##       Party.fctr.R Party.fctr.D Party.fctr.NA
## Test            NA           NA          1392
## Train         2617         2951            NA
##       Party.fctr.R Party.fctr.D Party.fctr.NA
## Test            NA           NA             1
## Train    0.4700072    0.5299928            NA

## [1] "elapsed Time (secs): 8.577000"
## Loading required package: caTools
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "elapsed Time (secs): 127.998000"
## [1] "elapsed Time (secs): 127.998000"
##          label step_major step_minor label_minor     bgn     end elapsed
## 2 inspect.data          2          0           0  24.134 162.973 138.839
## 3   scrub.data          2          1           1 162.974      NA      NA

Step 2.1: scrub data

## [1] "numeric data missing in glbObsAll: "
##        YOB Party.fctr 
##        415       1392 
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##          Gender          Income HouseholdStatus  EducationLevel 
##             143            1273             552            1067 
##           Party         Q124742         Q124122         Q123464 
##              NA            4340            3114            2912 
##         Q123621         Q122769         Q122770         Q122771 
##            3018            2778            2597            2579 
##         Q122120         Q121699         Q121700         Q120978 
##            2552            2279            2328            2303 
##         Q121011         Q120379         Q120650         Q120472 
##            2256            2361            2283            2433 
##         Q120194         Q120012         Q120014         Q119334 
##            2603            2344            2571            2477 
##         Q119851         Q119650         Q118892         Q118117 
##            2243            2374            2206            2342 
##         Q118232         Q118233         Q118237         Q117186 
##            3018            2659            2592            2845 
##         Q117193         Q116797         Q116881         Q116953 
##            2799            2771            2889            2848 
##         Q116601         Q116441         Q116448         Q116197 
##            2606            2684            2730            2657 
##         Q115602         Q115777         Q115610         Q115611 
##            2619            2785            2637            2443 
##         Q115899         Q115390         Q114961         Q114748 
##            2789            2860            2687            2462 
##         Q115195         Q114517         Q114386         Q113992 
##            2647            2567            2686            2502 
##         Q114152         Q113583         Q113584         Q113181 
##            2829            2632            2654            2576 
##         Q112478         Q112512         Q112270         Q111848 
##            2790            2676            2820            2449 
##         Q111580         Q111220         Q110740         Q109367 
##            2686            2563            2479            2624 
##         Q108950         Q109244         Q108855         Q108617 
##            2641            2731            3008            2696 
##         Q108856         Q108754         Q108342         Q108343 
##            3007            2770            2760            2736 
##         Q107869         Q107491         Q106993         Q106997 
##            2762            2667            2676            2702 
##         Q106272         Q106388         Q106389         Q106042 
##            2722            2818            2871            2762 
##         Q105840         Q105655         Q104996         Q103293 
##            2876            2612            2620            2674 
##         Q102906         Q102674         Q102687         Q102289 
##            2840            2864            2712            2790 
##         Q102089         Q101162         Q101163         Q101596 
##            2736            2816            2995            2824 
##         Q100689         Q100680         Q100562          Q99982 
##            2568            2787            2793            2871 
##         Q100010          Q99716          Q99581          Q99480 
##            2688            2790            2690            2700 
##          Q98869          Q98578          Q98059          Q98078 
##            2906            2867            2629            2945 
##          Q98197          Q96024 
##            2836            2858
##            label step_major step_minor label_minor     bgn     end elapsed
## 3     scrub.data          2          1           1 162.974 196.936  33.962
## 4 transform.data          2          2           2 196.937      NA      NA

Step 2.2: transform data

##              label step_major step_minor label_minor     bgn    end
## 4   transform.data          2          2           2 196.937 196.98
## 5 extract.features          3          0           0 196.981     NA
##   elapsed
## 4   0.043
## 5      NA

Step 3.0: extract features

##                       label step_major step_minor label_minor     bgn
## 5          extract.features          3          0           0 196.981
## 6 extract.features.datetime          3          1           1 197.003
##       end elapsed
## 5 197.002   0.021
## 6      NA      NA

Step 3.1: extract features datetime

##                           label step_major step_minor label_minor     bgn
## 1 extract.features.datetime.bgn          1          0           0 197.031
##   end elapsed
## 1  NA      NA
##                       label step_major step_minor label_minor     bgn
## 6 extract.features.datetime          3          1           1 197.003
## 7    extract.features.image          3          2           2 197.042
##       end elapsed
## 6 197.041   0.038
## 7      NA      NA

Step 3.2: extract features image

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

##                        label step_major step_minor label_minor     bgn end
## 1 extract.features.image.bgn          1          0           0 197.076  NA
##   elapsed
## 1      NA
##                        label step_major step_minor label_minor     bgn
## 1 extract.features.image.bgn          1          0           0 197.076
## 2 extract.features.image.end          2          0           0 197.085
##       end elapsed
## 1 197.085   0.009
## 2      NA      NA
##                        label step_major step_minor label_minor     bgn
## 1 extract.features.image.bgn          1          0           0 197.076
## 2 extract.features.image.end          2          0           0 197.085
##       end elapsed
## 1 197.085   0.009
## 2      NA      NA
##                    label step_major step_minor label_minor     bgn     end
## 7 extract.features.image          3          2           2 197.042 197.095
## 8 extract.features.price          3          3           3 197.096      NA
##   elapsed
## 7   0.054
## 8      NA

Step 3.3: extract features price

##                        label step_major step_minor label_minor     bgn end
## 1 extract.features.price.bgn          1          0           0 197.123  NA
##   elapsed
## 1      NA
##                    label step_major step_minor label_minor     bgn     end
## 8 extract.features.price          3          3           3 197.096 197.131
## 9  extract.features.text          3          4           4 197.132      NA
##   elapsed
## 8   0.036
## 9      NA

Step 3.4: extract features text

##                       label step_major step_minor label_minor     bgn end
## 1 extract.features.text.bgn          1          0           0 197.173  NA
##   elapsed
## 1      NA
##                      label step_major step_minor label_minor     bgn   end
## 9    extract.features.text          3          4           4 197.132 197.2
## 10 extract.features.string          3          5           5 197.200    NA
##    elapsed
## 9    0.068
## 10      NA

Step 3.5: extract features string

##                         label step_major step_minor label_minor     bgn
## 1 extract.features.string.bgn          1          0           0 197.233
##   end elapsed
## 1  NA      NA
##                                       label step_major step_minor
## 1               extract.features.string.bgn          1          0
## 2 extract.features.stringfactorize.str.vars          2          0
##   label_minor     bgn     end elapsed
## 1           0 197.233 197.244   0.012
## 2           0 197.245      NA      NA
##            Gender            Income   HouseholdStatus    EducationLevel 
##          "Gender"          "Income" "HouseholdStatus"  "EducationLevel" 
##             Party           Q124742           Q124122           Q123464 
##           "Party"         "Q124742"         "Q124122"         "Q123464" 
##           Q123621           Q122769           Q122770           Q122771 
##         "Q123621"         "Q122769"         "Q122770"         "Q122771" 
##           Q122120           Q121699           Q121700           Q120978 
##         "Q122120"         "Q121699"         "Q121700"         "Q120978" 
##           Q121011           Q120379           Q120650           Q120472 
##         "Q121011"         "Q120379"         "Q120650"         "Q120472" 
##           Q120194           Q120012           Q120014           Q119334 
##         "Q120194"         "Q120012"         "Q120014"         "Q119334" 
##           Q119851           Q119650           Q118892           Q118117 
##         "Q119851"         "Q119650"         "Q118892"         "Q118117" 
##           Q118232           Q118233           Q118237           Q117186 
##         "Q118232"         "Q118233"         "Q118237"         "Q117186" 
##           Q117193           Q116797           Q116881           Q116953 
##         "Q117193"         "Q116797"         "Q116881"         "Q116953" 
##           Q116601           Q116441           Q116448           Q116197 
##         "Q116601"         "Q116441"         "Q116448"         "Q116197" 
##           Q115602           Q115777           Q115610           Q115611 
##         "Q115602"         "Q115777"         "Q115610"         "Q115611" 
##           Q115899           Q115390           Q114961           Q114748 
##         "Q115899"         "Q115390"         "Q114961"         "Q114748" 
##           Q115195           Q114517           Q114386           Q113992 
##         "Q115195"         "Q114517"         "Q114386"         "Q113992" 
##           Q114152           Q113583           Q113584           Q113181 
##         "Q114152"         "Q113583"         "Q113584"         "Q113181" 
##           Q112478           Q112512           Q112270           Q111848 
##         "Q112478"         "Q112512"         "Q112270"         "Q111848" 
##           Q111580           Q111220           Q110740           Q109367 
##         "Q111580"         "Q111220"         "Q110740"         "Q109367" 
##           Q108950           Q109244           Q108855           Q108617 
##         "Q108950"         "Q109244"         "Q108855"         "Q108617" 
##           Q108856           Q108754           Q108342           Q108343 
##         "Q108856"         "Q108754"         "Q108342"         "Q108343" 
##           Q107869           Q107491           Q106993           Q106997 
##         "Q107869"         "Q107491"         "Q106993"         "Q106997" 
##           Q106272           Q106388           Q106389           Q106042 
##         "Q106272"         "Q106388"         "Q106389"         "Q106042" 
##           Q105840           Q105655           Q104996           Q103293 
##         "Q105840"         "Q105655"         "Q104996"         "Q103293" 
##           Q102906           Q102674           Q102687           Q102289 
##         "Q102906"         "Q102674"         "Q102687"         "Q102289" 
##           Q102089           Q101162           Q101163           Q101596 
##         "Q102089"         "Q101162"         "Q101163"         "Q101596" 
##           Q100689           Q100680           Q100562            Q99982 
##         "Q100689"         "Q100680"         "Q100562"          "Q99982" 
##           Q100010            Q99716            Q99581            Q99480 
##         "Q100010"          "Q99716"          "Q99581"          "Q99480" 
##            Q98869            Q98578            Q98059            Q98078 
##          "Q98869"          "Q98578"          "Q98059"          "Q98078" 
##            Q98197            Q96024              .src 
##          "Q98197"          "Q96024"            ".src"
##                      label step_major step_minor label_minor     bgn
## 10 extract.features.string          3          5           5 197.200
## 11    extract.features.end          3          6           6 197.269
##        end elapsed
## 10 197.268   0.068
## 11      NA      NA

Step 3.6: extract features end

## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0

##                   label step_major step_minor label_minor     bgn    end
## 11 extract.features.end          3          6           6 197.269 198.18
## 12  manage.missing.data          4          0           0 198.181     NA
##    elapsed
## 11   0.912
## 12      NA

Step 4.0: manage missing data

## [1] "numeric data missing in glbObsAll: "
##        YOB Party.fctr 
##        415       1392 
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##          Gender          Income HouseholdStatus  EducationLevel 
##             143            1273             552            1067 
##           Party         Q124742         Q124122         Q123464 
##              NA            4340            3114            2912 
##         Q123621         Q122769         Q122770         Q122771 
##            3018            2778            2597            2579 
##         Q122120         Q121699         Q121700         Q120978 
##            2552            2279            2328            2303 
##         Q121011         Q120379         Q120650         Q120472 
##            2256            2361            2283            2433 
##         Q120194         Q120012         Q120014         Q119334 
##            2603            2344            2571            2477 
##         Q119851         Q119650         Q118892         Q118117 
##            2243            2374            2206            2342 
##         Q118232         Q118233         Q118237         Q117186 
##            3018            2659            2592            2845 
##         Q117193         Q116797         Q116881         Q116953 
##            2799            2771            2889            2848 
##         Q116601         Q116441         Q116448         Q116197 
##            2606            2684            2730            2657 
##         Q115602         Q115777         Q115610         Q115611 
##            2619            2785            2637            2443 
##         Q115899         Q115390         Q114961         Q114748 
##            2789            2860            2687            2462 
##         Q115195         Q114517         Q114386         Q113992 
##            2647            2567            2686            2502 
##         Q114152         Q113583         Q113584         Q113181 
##            2829            2632            2654            2576 
##         Q112478         Q112512         Q112270         Q111848 
##            2790            2676            2820            2449 
##         Q111580         Q111220         Q110740         Q109367 
##            2686            2563            2479            2624 
##         Q108950         Q109244         Q108855         Q108617 
##            2641            2731            3008            2696 
##         Q108856         Q108754         Q108342         Q108343 
##            3007            2770            2760            2736 
##         Q107869         Q107491         Q106993         Q106997 
##            2762            2667            2676            2702 
##         Q106272         Q106388         Q106389         Q106042 
##            2722            2818            2871            2762 
##         Q105840         Q105655         Q104996         Q103293 
##            2876            2612            2620            2674 
##         Q102906         Q102674         Q102687         Q102289 
##            2840            2864            2712            2790 
##         Q102089         Q101162         Q101163         Q101596 
##            2736            2816            2995            2824 
##         Q100689         Q100680         Q100562          Q99982 
##            2568            2787            2793            2871 
##         Q100010          Q99716          Q99581          Q99480 
##            2688            2790            2690            2700 
##          Q98869          Q98578          Q98059          Q98078 
##            2906            2867            2629            2945 
##          Q98197          Q96024 
##            2836            2858
## [1] "numeric data missing in glbObsAll: "
##        YOB Party.fctr 
##        415       1392 
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##          Gender          Income HouseholdStatus  EducationLevel 
##             143            1273             552            1067 
##           Party         Q124742         Q124122         Q123464 
##              NA            4340            3114            2912 
##         Q123621         Q122769         Q122770         Q122771 
##            3018            2778            2597            2579 
##         Q122120         Q121699         Q121700         Q120978 
##            2552            2279            2328            2303 
##         Q121011         Q120379         Q120650         Q120472 
##            2256            2361            2283            2433 
##         Q120194         Q120012         Q120014         Q119334 
##            2603            2344            2571            2477 
##         Q119851         Q119650         Q118892         Q118117 
##            2243            2374            2206            2342 
##         Q118232         Q118233         Q118237         Q117186 
##            3018            2659            2592            2845 
##         Q117193         Q116797         Q116881         Q116953 
##            2799            2771            2889            2848 
##         Q116601         Q116441         Q116448         Q116197 
##            2606            2684            2730            2657 
##         Q115602         Q115777         Q115610         Q115611 
##            2619            2785            2637            2443 
##         Q115899         Q115390         Q114961         Q114748 
##            2789            2860            2687            2462 
##         Q115195         Q114517         Q114386         Q113992 
##            2647            2567            2686            2502 
##         Q114152         Q113583         Q113584         Q113181 
##            2829            2632            2654            2576 
##         Q112478         Q112512         Q112270         Q111848 
##            2790            2676            2820            2449 
##         Q111580         Q111220         Q110740         Q109367 
##            2686            2563            2479            2624 
##         Q108950         Q109244         Q108855         Q108617 
##            2641            2731            3008            2696 
##         Q108856         Q108754         Q108342         Q108343 
##            3007            2770            2760            2736 
##         Q107869         Q107491         Q106993         Q106997 
##            2762            2667            2676            2702 
##         Q106272         Q106388         Q106389         Q106042 
##            2722            2818            2871            2762 
##         Q105840         Q105655         Q104996         Q103293 
##            2876            2612            2620            2674 
##         Q102906         Q102674         Q102687         Q102289 
##            2840            2864            2712            2790 
##         Q102089         Q101162         Q101163         Q101596 
##            2736            2816            2995            2824 
##         Q100689         Q100680         Q100562          Q99982 
##            2568            2787            2793            2871 
##         Q100010          Q99716          Q99581          Q99480 
##            2688            2790            2690            2700 
##          Q98869          Q98578          Q98059          Q98078 
##            2906            2867            2629            2945 
##          Q98197          Q96024 
##            2836            2858
##                  label step_major step_minor label_minor     bgn     end
## 12 manage.missing.data          4          0           0 198.181 199.078
## 13        cluster.data          5          0           0 199.079      NA
##    elapsed
## 12   0.897
## 13      NA

Step 5.0: cluster data

##                      label step_major step_minor label_minor     bgn
## 13            cluster.data          5          0           0 199.079
## 14 partition.data.training          6          0           0 199.188
##        end elapsed
## 13 199.188   0.109
## 14      NA      NA

Step 6.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"
## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 0.13 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 0.13 secs"
## Loading required package: sampling
## 
## Attaching package: 'sampling'
## The following object is masked from 'package:caret':
## 
##     cluster
## [1] "lclgetMatrixCorrelation: duration: 40.914000 secs"
## [1] "cor of Fit vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 14.304000 secs"
## [1] "cor of New vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 50.199000 secs"
## [1] "cor of Fit vs. New: 1.0000"
## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 106.67 secs"
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA     1392
## Fit           2357             2091       NA
## OOB            594              526       NA
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA        1
## Fit      0.5299011        0.4700989       NA
## OOB      0.5303571        0.4696429       NA
##   Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6        SKn   1920    511    638     0.43165468    0.456250000
## 2        MKy   1296    298    371     0.29136691    0.266071429
## 1        MKn    516    136    169     0.11600719    0.121428571
## 3          N    367     83    102     0.08250899    0.074107143
## 7        SKy    147     53     65     0.03304856    0.047321429
## 4        PKn    150     30     37     0.03372302    0.026785714
## 5        PKy     52      9     10     0.01169065    0.008035714
##   .freqRatio.Tst
## 6    0.458333333
## 2    0.266522989
## 1    0.121408046
## 3    0.073275862
## 7    0.046695402
## 4    0.026580460
## 5    0.007183908
## [1] "glbObsAll: "
## [1] 6960  209
## [1] "glbObsTrn: "
## [1] 5568  209
## [1] "glbObsFit: "
## [1] 4448  208
## [1] "glbObsOOB: "
## [1] 1120  208
## [1] "glbObsNew: "
## [1] 1392  208
## [1] "partition.data.training chunk: teardown: elapsed: 107.54 secs"
##                      label step_major step_minor label_minor     bgn
## 14 partition.data.training          6          0           0 199.188
## 15         select.features          7          0           0 306.793
##        end elapsed
## 14 306.792 107.604
## 15      NA      NA

Step 7.0: select features

## [1] "cor(Q108855.fctr, Q108856.fctr)=0.7430"
## [1] "cor(Party.fctr, Q108855.fctr)=-0.0371"
## [1] "cor(Party.fctr, Q108856.fctr)=-0.0140"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108856.fctr as highly correlated with Q108855.fctr
## [1] "cor(Q122770.fctr, Q122771.fctr)=0.7379"
## [1] "cor(Party.fctr, Q122770.fctr)=-0.0195"
## [1] "cor(Party.fctr, Q122771.fctr)=-0.0348"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q122770.fctr as highly correlated with Q122771.fctr
## [1] "cor(Q106272.fctr, Q106388.fctr)=0.7339"
## [1] "cor(Party.fctr, Q106272.fctr)=-0.0401"
## [1] "cor(Party.fctr, Q106388.fctr)=-0.0342"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q106388.fctr as highly correlated with Q106272.fctr
## [1] "cor(Q100680.fctr, Q100689.fctr)=0.7292"
## [1] "cor(Party.fctr, Q100680.fctr)=0.0158"
## [1] "cor(Party.fctr, Q100689.fctr)=0.0257"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100680.fctr as highly correlated with Q100689.fctr
## [1] "cor(Q120472.fctr, Q120650.fctr)=0.7126"
## [1] "cor(Party.fctr, Q120472.fctr)=-0.0462"
## [1] "cor(Party.fctr, Q120650.fctr)=-0.0271"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q120650.fctr as highly correlated with Q120472.fctr
## [1] "cor(Q123464.fctr, Q123621.fctr)=0.7078"
## [1] "cor(Party.fctr, Q123464.fctr)=-0.0136"
## [1] "cor(Party.fctr, Q123621.fctr)=-0.0255"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q123464.fctr as highly correlated with Q123621.fctr
## [1] "cor(Q108754.fctr, Q108855.fctr)=0.7005"
## [1] "cor(Party.fctr, Q108754.fctr)=-0.0081"
## [1] "cor(Party.fctr, Q108855.fctr)=-0.0371"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108754.fctr as highly correlated with Q108855.fctr
##                      cor.y exclude.as.feat    cor.y.abs   cor.high.X
## Q109244.fctr  0.1203812469               0 0.1203812469         <NA>
## Hhold.fctr    0.0511386673               0 0.0511386673         <NA>
## Edn.fctr      0.0359295351               0 0.0359295351         <NA>
## Q101163.fctr  0.0295046473               0 0.0295046473         <NA>
## Q100689.fctr  0.0256915080               0 0.0256915080         <NA>
## Q120379.fctr  0.0206291292               0 0.0206291292         <NA>
## Q121699.fctr  0.0196933075               0 0.0196933075         <NA>
## Q105840.fctr  0.0195569165               0 0.0195569165         <NA>
## Q113583.fctr  0.0191894717               0 0.0191894717         <NA>
## Q115195.fctr  0.0174522586               0 0.0174522586         <NA>
## Q102089.fctr  0.0174087944               0 0.0174087944         <NA>
## Q114386.fctr  0.0168013326               0 0.0168013326         <NA>
## Q100680.fctr  0.0157762454               0 0.0157762454 Q100689.fctr
## Q108342.fctr  0.0151842510               0 0.0151842510         <NA>
## Q111848.fctr  0.0141099384               0 0.0141099384         <NA>
## YOB.Age.fctr  0.0129198495               0 0.0129198495         <NA>
## Q118892.fctr  0.0125250379               0 0.0125250379         <NA>
## Q102687.fctr  0.0120079165               0 0.0120079165         <NA>
## Q115390.fctr  0.0119300319               0 0.0119300319         <NA>
## Q119851.fctr  0.0093381833               0 0.0093381833         <NA>
## Q114517.fctr  0.0084741753               0 0.0084741753         <NA>
## Q120012.fctr  0.0084652930               0 0.0084652930         <NA>
## Q109367.fctr  0.0080456026               0 0.0080456026         <NA>
## Q114961.fctr  0.0079206587               0 0.0079206587         <NA>
## Q121700.fctr  0.0067756198               0 0.0067756198         <NA>
## Q124122.fctr  0.0061257448               0 0.0061257448         <NA>
## Q111220.fctr  0.0055758571               0 0.0055758571         <NA>
## Q113992.fctr  0.0041479796               0 0.0041479796         <NA>
## Q121011.fctr  0.0037329030               0 0.0037329030         <NA>
## Q106042.fctr  0.0032327194               0 0.0032327194         <NA>
## Q116448.fctr  0.0031731051               0 0.0031731051         <NA>
## Q116601.fctr  0.0022379241               0 0.0022379241         <NA>
## Q104996.fctr  0.0012202806               0 0.0012202806         <NA>
## Q102906.fctr  0.0011540297               0 0.0011540297         <NA>
## Q113584.fctr  0.0011387024               0 0.0011387024         <NA>
## Q108950.fctr  0.0010567028               0 0.0010567028         <NA>
## Q102674.fctr  0.0009759844               0 0.0009759844         <NA>
## Q103293.fctr  0.0005915534               0 0.0005915534         <NA>
## Q112478.fctr  0.0001517248               0 0.0001517248         <NA>
## Q114748.fctr -0.0008477228               0 0.0008477228         <NA>
## Q107491.fctr -0.0014031814               0 0.0014031814         <NA>
## Q100562.fctr -0.0017132769               0 0.0017132769         <NA>
## Q108617.fctr -0.0024119725               0 0.0024119725         <NA>
## Q100010.fctr -0.0024291540               0 0.0024291540         <NA>
## Q115602.fctr -0.0027844465               0 0.0027844465         <NA>
## Q116953.fctr -0.0029786716               0 0.0029786716         <NA>
## Q115610.fctr -0.0035255582               0 0.0035255582         <NA>
## Q106997.fctr -0.0041749086               0 0.0041749086         <NA>
## Q120978.fctr -0.0044187616               0 0.0044187616         <NA>
## Q112512.fctr -0.0056768212               0 0.0056768212         <NA>
## Q108343.fctr -0.0060665340               0 0.0060665340         <NA>
## Q106389.fctr -0.0077498918               0 0.0077498918         <NA>
## .rnorm       -0.0078039520               0 0.0078039520         <NA>
## Q108754.fctr -0.0080847764               0 0.0080847764 Q108855.fctr
## Q101162.fctr -0.0099412952               0 0.0099412952         <NA>
## Q115777.fctr -0.0101315203               0 0.0101315203         <NA>
## Q124742.fctr -0.0111642906               0 0.0111642906         <NA>
## Q116797.fctr -0.0112749656               0 0.0112749656         <NA>
## Q112270.fctr -0.0116157798               0 0.0116157798         <NA>
## YOB          -0.0116828198               1 0.0116828198         <NA>
## Q118237.fctr -0.0117079669               0 0.0117079669         <NA>
## Q119650.fctr -0.0125645475               0 0.0125645475         <NA>
## Q111580.fctr -0.0132382335               0 0.0132382335         <NA>
## Q123464.fctr -0.0136140083               0 0.0136140083 Q123621.fctr
## Q117193.fctr -0.0138241599               0 0.0138241599         <NA>
## Q108856.fctr -0.0140363785               0 0.0140363785 Q108855.fctr
## Q118233.fctr -0.0147269325               0 0.0147269325         <NA>
## Q102289.fctr -0.0155850393               0 0.0155850393         <NA>
## Q116197.fctr -0.0158561766               0 0.0158561766         <NA>
## Income.fctr  -0.0159635458               0 0.0159635458         <NA>
## Q118232.fctr -0.0171321152               0 0.0171321152         <NA>
## Q120194.fctr -0.0172986920               0 0.0172986920         <NA>
## Q114152.fctr -0.0175013163               0 0.0175013163         <NA>
## Q122770.fctr -0.0194639697               0 0.0194639697 Q122771.fctr
## Q117186.fctr -0.0198853672               0 0.0198853672         <NA>
## Q105655.fctr -0.0198994078               0 0.0198994078         <NA>
## Q106993.fctr -0.0207428635               0 0.0207428635         <NA>
## Q119334.fctr -0.0226894034               0 0.0226894034         <NA>
## Q122120.fctr -0.0229287700               0 0.0229287700         <NA>
## Q116441.fctr -0.0237358205               0 0.0237358205         <NA>
## Q118117.fctr -0.0253544150               0 0.0253544150         <NA>
## Q123621.fctr -0.0255329743               0 0.0255329743         <NA>
## Q122769.fctr -0.0259739146               0 0.0259739146         <NA>
## Q120650.fctr -0.0270889067               0 0.0270889067 Q120472.fctr
## .pos         -0.0302037138               1 0.0302037138         <NA>
## USER_ID      -0.0302304868               1 0.0302304868         <NA>
## Q107869.fctr -0.0304661021               0 0.0304661021         <NA>
## Q120014.fctr -0.0318620439               0 0.0318620439         <NA>
## Q115899.fctr -0.0324177950               0 0.0324177950         <NA>
## Q106388.fctr -0.0341579350               0 0.0341579350 Q106272.fctr
## Q122771.fctr -0.0348421015               0 0.0348421015         <NA>
## Q108855.fctr -0.0370970211               0 0.0370970211         <NA>
## Q110740.fctr -0.0380691243               0 0.0380691243         <NA>
## Q106272.fctr -0.0400926462               0 0.0400926462         <NA>
## Q101596.fctr -0.0409784077               0 0.0409784077         <NA>
## Q116881.fctr -0.0416860293               0 0.0416860293         <NA>
## Q120472.fctr -0.0462030674               0 0.0462030674         <NA>
## Q113181.fctr -0.0808753072               0 0.0808753072         <NA>
## Q115611.fctr -0.0904468203               0 0.0904468203         <NA>
## Gender.fctr  -0.1027400851               0 0.1027400851         <NA>
##              freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## Q109244.fctr  1.125916    0.05387931   FALSE FALSE            FALSE
## Hhold.fctr    1.525094    0.12571839   FALSE FALSE            FALSE
## Edn.fctr      1.392610    0.14367816   FALSE FALSE            FALSE
## Q101163.fctr  1.327394    0.05387931   FALSE FALSE            FALSE
## Q100689.fctr  1.029800    0.05387931   FALSE FALSE            FALSE
## Q120379.fctr  1.046326    0.05387931   FALSE FALSE            FALSE
## Q121699.fctr  1.507127    0.05387931   FALSE FALSE            FALSE
## Q105840.fctr  1.275362    0.05387931   FALSE FALSE            FALSE
## Q113583.fctr  1.102515    0.05387931   FALSE FALSE            FALSE
## Q115195.fctr  1.065496    0.05387931   FALSE FALSE            FALSE
## Q102089.fctr  1.055963    0.05387931   FALSE FALSE            FALSE
## Q114386.fctr  1.092072    0.05387931   FALSE FALSE            FALSE
## Q100680.fctr  1.102386    0.05387931   FALSE FALSE            FALSE
## Q108342.fctr  1.048292    0.05387931   FALSE FALSE            FALSE
## Q111848.fctr  1.113602    0.05387931   FALSE FALSE            FALSE
## YOB.Age.fctr  1.005794    0.16163793   FALSE FALSE            FALSE
## Q118892.fctr  1.347380    0.05387931   FALSE FALSE            FALSE
## Q102687.fctr  1.256545    0.05387931   FALSE FALSE            FALSE
## Q115390.fctr  1.150505    0.05387931   FALSE FALSE            FALSE
## Q119851.fctr  1.244519    0.05387931   FALSE FALSE            FALSE
## Q114517.fctr  1.183374    0.05387931   FALSE FALSE            FALSE
## Q120012.fctr  1.047185    0.05387931   FALSE FALSE            FALSE
## Q109367.fctr  1.008571    0.05387931   FALSE FALSE            FALSE
## Q114961.fctr  1.250436    0.05387931   FALSE FALSE            FALSE
## Q121700.fctr  1.708221    0.05387931   FALSE FALSE             TRUE
## Q124122.fctr  1.412807    0.05387931   FALSE FALSE             TRUE
## Q111220.fctr  1.262849    0.05387931   FALSE FALSE             TRUE
## Q113992.fctr  1.267442    0.05387931   FALSE FALSE             TRUE
## Q121011.fctr  1.153676    0.05387931   FALSE FALSE             TRUE
## Q106042.fctr  1.247738    0.05387931   FALSE FALSE             TRUE
## Q116448.fctr  1.161031    0.05387931   FALSE FALSE             TRUE
## Q116601.fctr  1.394914    0.05387931   FALSE FALSE             TRUE
## Q104996.fctr  1.173840    0.05387931   FALSE FALSE             TRUE
## Q102906.fctr  1.053396    0.05387931   FALSE FALSE             TRUE
## Q113584.fctr  1.212486    0.05387931   FALSE FALSE             TRUE
## Q108950.fctr  1.103872    0.05387931   FALSE FALSE             TRUE
## Q102674.fctr  1.073412    0.05387931   FALSE FALSE             TRUE
## Q103293.fctr  1.122287    0.05387931   FALSE FALSE             TRUE
## Q112478.fctr  1.113648    0.05387931   FALSE FALSE             TRUE
## Q114748.fctr  1.051125    0.05387931   FALSE FALSE             TRUE
## Q107491.fctr  1.419021    0.05387931   FALSE FALSE             TRUE
## Q100562.fctr  1.217215    0.05387931   FALSE FALSE             TRUE
## Q108617.fctr  1.390618    0.05387931   FALSE FALSE             TRUE
## Q100010.fctr  1.268156    0.05387931   FALSE FALSE             TRUE
## Q115602.fctr  1.322302    0.05387931   FALSE FALSE             TRUE
## Q116953.fctr  1.039180    0.05387931   FALSE FALSE             TRUE
## Q115610.fctr  1.359695    0.05387931   FALSE FALSE             TRUE
## Q106997.fctr  1.177632    0.05387931   FALSE FALSE             TRUE
## Q120978.fctr  1.131963    0.05387931   FALSE FALSE             TRUE
## Q112512.fctr  1.299253    0.05387931   FALSE FALSE             TRUE
## Q108343.fctr  1.064910    0.05387931   FALSE FALSE             TRUE
## Q106389.fctr  1.341307    0.05387931   FALSE FALSE             TRUE
## .rnorm        1.000000  100.00000000   FALSE FALSE            FALSE
## Q108754.fctr  1.008090    0.05387931   FALSE FALSE            FALSE
## Q101162.fctr  1.103229    0.05387931   FALSE FALSE            FALSE
## Q115777.fctr  1.140288    0.05387931   FALSE FALSE            FALSE
## Q124742.fctr  2.565379    0.05387931   FALSE FALSE            FALSE
## Q116797.fctr  1.009589    0.05387931   FALSE FALSE            FALSE
## Q112270.fctr  1.254284    0.05387931   FALSE FALSE            FALSE
## YOB           1.027559    1.41882184   FALSE FALSE            FALSE
## Q118237.fctr  1.088017    0.05387931   FALSE FALSE            FALSE
## Q119650.fctr  1.456978    0.05387931   FALSE FALSE            FALSE
## Q111580.fctr  1.024977    0.05387931   FALSE FALSE            FALSE
## Q123464.fctr  1.326681    0.05387931   FALSE FALSE            FALSE
## Q117193.fctr  1.140665    0.05387931   FALSE FALSE            FALSE
## Q108856.fctr  1.080645    0.05387931   FALSE FALSE            FALSE
## Q118233.fctr  1.199142    0.05387931   FALSE FALSE            FALSE
## Q102289.fctr  1.033482    0.05387931   FALSE FALSE            FALSE
## Q116197.fctr  1.073778    0.05387931   FALSE FALSE            FALSE
## Income.fctr   1.256724    0.12571839   FALSE FALSE            FALSE
## Q118232.fctr  1.365812    0.05387931   FALSE FALSE            FALSE
## Q120194.fctr  1.016716    0.05387931   FALSE FALSE            FALSE
## Q114152.fctr  1.027617    0.05387931   FALSE FALSE            FALSE
## Q122770.fctr  1.008802    0.05387931   FALSE FALSE            FALSE
## Q117186.fctr  1.053878    0.05387931   FALSE FALSE            FALSE
## Q105655.fctr  1.079316    0.05387931   FALSE FALSE            FALSE
## Q106993.fctr  1.327392    0.05387931   FALSE FALSE            FALSE
## Q119334.fctr  1.081498    0.05387931   FALSE FALSE            FALSE
## Q122120.fctr  1.297443    0.05387931   FALSE FALSE            FALSE
## Q116441.fctr  1.019645    0.05387931   FALSE FALSE            FALSE
## Q118117.fctr  1.174006    0.05387931   FALSE FALSE            FALSE
## Q123621.fctr  1.466381    0.05387931   FALSE FALSE            FALSE
## Q122769.fctr  1.060606    0.05387931   FALSE FALSE            FALSE
## Q120650.fctr  1.896247    0.05387931   FALSE FALSE            FALSE
## .pos          1.000000  100.00000000   FALSE FALSE            FALSE
## USER_ID       1.000000  100.00000000   FALSE FALSE            FALSE
## Q107869.fctr  1.211050    0.05387931   FALSE FALSE            FALSE
## Q120014.fctr  1.044944    0.05387931   FALSE FALSE            FALSE
## Q115899.fctr  1.197849    0.05387931   FALSE FALSE            FALSE
## Q106388.fctr  1.065033    0.05387931   FALSE FALSE            FALSE
## Q122771.fctr  1.414753    0.05387931   FALSE FALSE            FALSE
## Q108855.fctr  1.273980    0.05387931   FALSE FALSE            FALSE
## Q110740.fctr  1.050779    0.05387931   FALSE FALSE            FALSE
## Q106272.fctr  1.116536    0.05387931   FALSE FALSE            FALSE
## Q101596.fctr  1.041667    0.05387931   FALSE FALSE            FALSE
## Q116881.fctr  1.010066    0.05387931   FALSE FALSE            FALSE
## Q120472.fctr  1.292633    0.05387931   FALSE FALSE            FALSE
## Q113181.fctr  1.006354    0.05387931   FALSE FALSE            FALSE
## Q115611.fctr  1.194859    0.05387931   FALSE FALSE            FALSE
## Gender.fctr   1.561033    0.05387931   FALSE FALSE            FALSE
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## [1] cor.y            exclude.as.feat  cor.y.abs        cor.high.X      
## [5] freqRatio        percentUnique    zeroVar          nzv             
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "numeric data missing in glbObsAll: "
##        YOB Party.fctr 
##        415       1392 
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##          Gender          Income HouseholdStatus  EducationLevel 
##             143            1273             552            1067 
##           Party         Q124742         Q124122         Q123464 
##              NA            4340            3114            2912 
##         Q123621         Q122769         Q122770         Q122771 
##            3018            2778            2597            2579 
##         Q122120         Q121699         Q121700         Q120978 
##            2552            2279            2328            2303 
##         Q121011         Q120379         Q120650         Q120472 
##            2256            2361            2283            2433 
##         Q120194         Q120012         Q120014         Q119334 
##            2603            2344            2571            2477 
##         Q119851         Q119650         Q118892         Q118117 
##            2243            2374            2206            2342 
##         Q118232         Q118233         Q118237         Q117186 
##            3018            2659            2592            2845 
##         Q117193         Q116797         Q116881         Q116953 
##            2799            2771            2889            2848 
##         Q116601         Q116441         Q116448         Q116197 
##            2606            2684            2730            2657 
##         Q115602         Q115777         Q115610         Q115611 
##            2619            2785            2637            2443 
##         Q115899         Q115390         Q114961         Q114748 
##            2789            2860            2687            2462 
##         Q115195         Q114517         Q114386         Q113992 
##            2647            2567            2686            2502 
##         Q114152         Q113583         Q113584         Q113181 
##            2829            2632            2654            2576 
##         Q112478         Q112512         Q112270         Q111848 
##            2790            2676            2820            2449 
##         Q111580         Q111220         Q110740         Q109367 
##            2686            2563            2479            2624 
##         Q108950         Q109244         Q108855         Q108617 
##            2641            2731            3008            2696 
##         Q108856         Q108754         Q108342         Q108343 
##            3007            2770            2760            2736 
##         Q107869         Q107491         Q106993         Q106997 
##            2762            2667            2676            2702 
##         Q106272         Q106388         Q106389         Q106042 
##            2722            2818            2871            2762 
##         Q105840         Q105655         Q104996         Q103293 
##            2876            2612            2620            2674 
##         Q102906         Q102674         Q102687         Q102289 
##            2840            2864            2712            2790 
##         Q102089         Q101162         Q101163         Q101596 
##            2736            2816            2995            2824 
##         Q100689         Q100680         Q100562          Q99982 
##            2568            2787            2793            2871 
##         Q100010          Q99716          Q99581          Q99480 
##            2688            2790            2690            2700 
##          Q98869          Q98578          Q98059          Q98078 
##            2906            2867            2629            2945 
##          Q98197          Q96024            .lcn 
##            2836            2858            1392
## [1] "glb_feats_df:"
## [1] 100  12
##                    id exclude.as.feat rsp_var
## Party.fctr Party.fctr            TRUE    TRUE
##                    id       cor.y exclude.as.feat  cor.y.abs cor.high.X
## USER_ID       USER_ID -0.03023049            TRUE 0.03023049       <NA>
## Party.fctr Party.fctr          NA            TRUE         NA       <NA>
##            freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## USER_ID            1           100   FALSE FALSE            FALSE
## Party.fctr        NA            NA      NA    NA               NA
##            interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID                  NA                   NA       FALSE   TRUE
## Party.fctr               NA                   NA          NA     NA
##            rsp_var
## USER_ID         NA
## Party.fctr    TRUE
## [1] "glb_feats_df vs. glbObsAll: "
## character(0)
## [1] "glbObsAll vs. glb_feats_df: "
## character(0)
##              label step_major step_minor label_minor     bgn    end
## 15 select.features          7          0           0 306.793 311.19
## 16      fit.models          8          0           0 311.190     NA
##    elapsed
## 15   4.397
## 16      NA

Step 8.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 311.686  NA      NA
# load(paste0(glbOut$pfx, "dsk.RData"))

get_model_sel_frmla <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

get_dsp_models_df <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#get_dsp_models_df()

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}
##              label step_major step_minor   label_minor     bgn     end
## 1 fit.models_0_bgn          1          0         setup 311.686 311.718
## 2 fit.models_0_MFO          1          1 myMFO_classfr 311.719      NA
##   elapsed
## 1   0.032
## 2      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: MFO###myMFO_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.428000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] R D
## Levels: R D
## [1] "unique.prob:"
## y
##         D         R 
## 0.5299011 0.4700989 
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.867000 secs"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "myfit_mdl: train diagnostics complete: 0.869000 secs"
## Loading required namespace: pROC
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
## [1] "in MFO.Classifier$prob"
##           R         D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989

##          Prediction
## Reference    R    D
##         R 2091    0
##         D 2357    0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4700989      0.0000000      0.4553427      0.4848945      0.5299011 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.0000000 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##           R         D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989

##          Prediction
## Reference   R   D
##         R 526   0
##         D 594   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.696429e-01   0.000000e+00   4.400805e-01   4.993651e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.999790e-01  9.194240e-131 
## [1] "myfit_mdl: predict complete: 3.814000 secs"
##                    id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm               0                      0.429
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.003             0.5            0            1
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                    0.5       0.6395473        0.4700989
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4553427             0.4848945             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            0            1             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.5       0.6391252        0.4696429
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4400805             0.4993651             0
## [1] "myfit_mdl: exit: 3.823000 secs"
##                 label step_major step_minor      label_minor     bgn
## 2    fit.models_0_MFO          1          1    myMFO_classfr 311.719
## 3 fit.models_0_Random          1          2 myrandom_classfr 315.547
##       end elapsed
## 2 315.547   3.828
## 3      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Random###myrandom_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.407000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.759000 secs"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "myfit_mdl: train diagnostics complete: 0.760000 secs"
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference    R    D
##         R 2091    0
##         D 2357    0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4700989      0.0000000      0.4553427      0.4848945      0.5299011 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.0000000 
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   R   D
##         R 526   0
##         D 594   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.696429e-01   0.000000e+00   4.400805e-01   4.993651e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.999790e-01  9.194240e-131 
## [1] "myfit_mdl: predict complete: 5.036000 secs"
##                          id  feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.346                 0.002       0.4942483
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.4619799    0.5265168       0.5073101                    0.6
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6395473        0.4700989             0.4553427
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.4848945             0        0.523569          0.5
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1     0.547138       0.5191202                    0.6       0.6391252
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.4696429             0.4400805             0.4993651
##   max.Kappa.OOB
## 1             0
## [1] "myfit_mdl: exit: 5.047000 secs"
# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")
##                            label step_major step_minor      label_minor
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
##       bgn     end elapsed
## 3 315.547 320.606    5.06
## 4 320.607      NA      NA
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] "    indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.708000 secs"
## Loading required package: glmnet
## Loading required package: Matrix
## Loaded glmnet 2.0-5
## Fitting alpha = 0.1, lambda = 0.00248 on full training set
## [1] "myfit_mdl: train complete: 1.625000 secs"

##             Length Class      Mode     
## a0           58    -none-     numeric  
## beta        232    dgCMatrix  S4       
## df           58    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       58    -none-     numeric  
## dev.ratio    58    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        4    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##     (Intercept)    Gender.fctrM  Q109244.fctrNo Q109244.fctrYes 
##       0.2665753      -0.2101506      -0.4308362       1.2139586 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"     "Gender.fctrF"    "Gender.fctrM"    "Q109244.fctrNo" 
## [5] "Q109244.fctrYes"
## [1] "myfit_mdl: train diagnostics complete: 1.723000 secs"

##          Prediction
## Reference    R    D
##         R 1950  141
##         D 1762  595
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.721673e-01   1.772539e-01   5.574714e-01   5.867683e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   8.241814e-09  7.365212e-302

##          Prediction
## Reference   R   D
##         R 484  42
##         D 447 147
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.633929e-01   1.605510e-01   5.337655e-01   5.926864e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   1.432605e-02   1.447405e-74 
## [1] "myfit_mdl: predict complete: 4.401000 secs"
##                           id                    feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.907                 0.064       0.5971118
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5480631    0.6461604       0.3580613                    0.6
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6720662        0.5721673             0.5574714
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5867683     0.1772539       0.5896897    0.5228137
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6565657       0.3658672                    0.6       0.6643789
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5633929             0.5337655             0.5926864
##   max.Kappa.OOB
## 1      0.160551
## [1] "myfit_mdl: exit: 4.413000 secs"
if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.684000 secs"
## Loading required package: rpart
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0225 on full training set
## [1] "myfit_mdl: train complete: 2.554000 secs"
## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 4448 
## 
##           CP nsplit rel error
## 1 0.08990913      0 1.0000000
## 2 0.05930177      1 0.9100909
## 3 0.02247728      2 0.8507891
## 
## Variable importance
## Q109244.fctrYes  Q109244.fctrNo    Gender.fctrM    Gender.fctrF 
##              83              15               1               1 
## 
## Node number 1: 4448 observations,    complexity param=0.08990913
##   predicted class=D  expected loss=0.4700989  P(node) =1
##     class counts:  2091  2357
##    probabilities: 0.470 0.530 
##   left son=2 (3712 obs) right son=3 (736 obs)
##   Primary splits:
##       Q109244.fctrYes < 0.5 to the left,  improve=136.83150, (0 missing)
##       Q109244.fctrNo  < 0.5 to the right, improve= 84.31128, (0 missing)
##       Gender.fctrM    < 0.5 to the right, improve= 24.39999, (0 missing)
##       Gender.fctrF    < 0.5 to the left,  improve= 22.65952, (0 missing)
## 
## Node number 2: 3712 observations,    complexity param=0.05930177
##   predicted class=R  expected loss=0.4746767  P(node) =0.8345324
##     class counts:  1950  1762
##    probabilities: 0.525 0.475 
##   left son=4 (1980 obs) right son=5 (1732 obs)
##   Primary splits:
##       Q109244.fctrNo < 0.5 to the right, improve=24.259840, (0 missing)
##       Gender.fctrM   < 0.5 to the right, improve=10.189980, (0 missing)
##       Gender.fctrF   < 0.5 to the left,  improve= 8.193561, (0 missing)
##   Surrogate splits:
##       Gender.fctrM < 0.5 to the right, agree=0.571, adj=0.080, (0 split)
##       Gender.fctrF < 0.5 to the left,  agree=0.563, adj=0.063, (0 split)
## 
## Node number 3: 736 observations
##   predicted class=D  expected loss=0.1915761  P(node) =0.1654676
##     class counts:   141   595
##    probabilities: 0.192 0.808 
## 
## Node number 4: 1980 observations
##   predicted class=R  expected loss=0.4212121  P(node) =0.4451439
##     class counts:  1146   834
##    probabilities: 0.579 0.421 
## 
## Node number 5: 1732 observations
##   predicted class=D  expected loss=0.4642032  P(node) =0.3893885
##     class counts:   804   928
##    probabilities: 0.464 0.536 
## 
## n= 4448 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 4448 2091 D (0.4700989 0.5299011)  
##   2) Q109244.fctrYes< 0.5 3712 1762 R (0.5253233 0.4746767)  
##     4) Q109244.fctrNo>=0.5 1980  834 R (0.5787879 0.4212121) *
##     5) Q109244.fctrNo< 0.5 1732  804 D (0.4642032 0.5357968) *
##   3) Q109244.fctrYes>=0.5 736  141 D (0.1915761 0.8084239) *
## [1] "myfit_mdl: train diagnostics complete: 3.394000 secs"

##          Prediction
## Reference    R    D
##         R 1950  141
##         D 1762  595
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.721673e-01   1.772539e-01   5.574714e-01   5.867683e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   8.241814e-09  7.365212e-302

##          Prediction
## Reference   R   D
##         R 484  42
##         D 447 147
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.633929e-01   1.605510e-01   5.337655e-01   5.926864e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   1.432605e-02   1.447405e-74 
## [1] "myfit_mdl: predict complete: 6.085000 secs"
##                     id                    feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr               5
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.862                 0.019       0.5971118
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5480631    0.6461604       0.3676308                    0.6
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6720662         0.600045             0.5574714
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5867683     0.1947896       0.5896897    0.5228137
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6565657       0.3774772                    0.6       0.6643789
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5633929             0.5337655             0.5926864
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1      0.160551          0.0124035      0.02559319
## [1] "myfit_mdl: exit: 6.100000 secs"
if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    
##                              label step_major step_minor label_minor
## 4   fit.models_0_Max.cor.Y.rcv.*X*          1          3      glmnet
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
##       bgn     end elapsed
## 4 320.607 331.158  10.551
## 5 331.158      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Gender.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr"
## [1] "myfit_mdl: setup complete: 0.683000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.000115 on full training set
## [1] "myfit_mdl: train complete: 5.107000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0            68   -none-     numeric  
## beta        2720   dgCMatrix  S4       
## df            68   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        68   -none-     numeric  
## dev.ratio     68   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        40   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                         (Intercept)                        Gender.fctrF 
##                         0.368820024                        -0.163130387 
##                        Gender.fctrM                      Q109244.fctrNo 
##                        -0.313865523                        -0.359818152 
##                     Q109244.fctrYes       Q109244.fctrNA:Q100689.fctrNo 
##                         1.135194801                         0.264178396 
##       Q109244.fctrNo:Q100689.fctrNo      Q109244.fctrYes:Q100689.fctrNo 
##                         0.270486538                        -0.142734265 
##      Q109244.fctrNA:Q100689.fctrYes      Q109244.fctrNo:Q100689.fctrYes 
##                         0.423668912                         0.317723889 
##     Q109244.fctrYes:Q100689.fctrYes       Q109244.fctrNA:Q106272.fctrNo 
##                         0.196982135                         0.122953815 
##       Q109244.fctrNo:Q106272.fctrNo      Q109244.fctrYes:Q106272.fctrNo 
##                         0.125251244                        -0.196534927 
##      Q109244.fctrNA:Q106272.fctrYes      Q109244.fctrNo:Q106272.fctrYes 
##                        -0.225589882                        -0.188148913 
##   Q109244.fctrNA:Q108855.fctrUmm...   Q109244.fctrNo:Q108855.fctrUmm... 
##                        -0.406823206                         0.149840328 
##  Q109244.fctrYes:Q108855.fctrUmm...     Q109244.fctrNA:Q108855.fctrYes! 
##                        -0.041370823                         0.041290322 
##     Q109244.fctrNo:Q108855.fctrYes!    Q109244.fctrYes:Q108855.fctrYes! 
##                        -0.121726184                        -0.172888782 
##      Q109244.fctrNA:Q120472.fctrArt      Q109244.fctrNo:Q120472.fctrArt 
##                         0.071673349                         0.059590359 
##     Q109244.fctrYes:Q120472.fctrArt  Q109244.fctrNA:Q120472.fctrScience 
##                         0.228458668                        -0.116255773 
##  Q109244.fctrNo:Q120472.fctrScience Q109244.fctrYes:Q120472.fctrScience 
##                        -0.006126843                         0.129080915 
##       Q109244.fctrNA:Q122771.fctrPc       Q109244.fctrNo:Q122771.fctrPc 
##                         0.106934804                        -0.198263813 
##      Q109244.fctrYes:Q122771.fctrPc       Q109244.fctrNA:Q122771.fctrPt 
##                        -0.302010432                        -0.050475157 
##       Q109244.fctrNo:Q122771.fctrPt      Q109244.fctrYes:Q122771.fctrPt 
##                        -0.463540726                        -0.592979565 
##       Q109244.fctrNA:Q123621.fctrNo       Q109244.fctrNo:Q123621.fctrNo 
##                        -0.146417007                         0.037175018 
##      Q109244.fctrYes:Q123621.fctrNo      Q109244.fctrNA:Q123621.fctrYes 
##                         0.609019125                        -0.063916402 
##      Q109244.fctrNo:Q123621.fctrYes     Q109244.fctrYes:Q123621.fctrYes 
##                        -0.119295785                         0.553231534 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
##  [1] "(Intercept)"                        
##  [2] "Gender.fctrF"                       
##  [3] "Gender.fctrM"                       
##  [4] "Q109244.fctrNo"                     
##  [5] "Q109244.fctrYes"                    
##  [6] "Q109244.fctrNA:Q100689.fctrNo"      
##  [7] "Q109244.fctrNo:Q100689.fctrNo"      
##  [8] "Q109244.fctrYes:Q100689.fctrNo"     
##  [9] "Q109244.fctrNA:Q100689.fctrYes"     
## [10] "Q109244.fctrNo:Q100689.fctrYes"     
## [11] "Q109244.fctrYes:Q100689.fctrYes"    
## [12] "Q109244.fctrNA:Q106272.fctrNo"      
## [13] "Q109244.fctrNo:Q106272.fctrNo"      
## [14] "Q109244.fctrYes:Q106272.fctrNo"     
## [15] "Q109244.fctrNA:Q106272.fctrYes"     
## [16] "Q109244.fctrNo:Q106272.fctrYes"     
## [17] "Q109244.fctrYes:Q106272.fctrYes"    
## [18] "Q109244.fctrNA:Q108855.fctrUmm..."  
## [19] "Q109244.fctrNo:Q108855.fctrUmm..."  
## [20] "Q109244.fctrYes:Q108855.fctrUmm..." 
## [21] "Q109244.fctrNA:Q108855.fctrYes!"    
## [22] "Q109244.fctrNo:Q108855.fctrYes!"    
## [23] "Q109244.fctrYes:Q108855.fctrYes!"   
## [24] "Q109244.fctrNA:Q120472.fctrArt"     
## [25] "Q109244.fctrNo:Q120472.fctrArt"     
## [26] "Q109244.fctrYes:Q120472.fctrArt"    
## [27] "Q109244.fctrNA:Q120472.fctrScience" 
## [28] "Q109244.fctrNo:Q120472.fctrScience" 
## [29] "Q109244.fctrYes:Q120472.fctrScience"
## [30] "Q109244.fctrNA:Q122771.fctrPc"      
## [31] "Q109244.fctrNo:Q122771.fctrPc"      
## [32] "Q109244.fctrYes:Q122771.fctrPc"     
## [33] "Q109244.fctrNA:Q122771.fctrPt"      
## [34] "Q109244.fctrNo:Q122771.fctrPt"      
## [35] "Q109244.fctrYes:Q122771.fctrPt"     
## [36] "Q109244.fctrNA:Q123621.fctrNo"      
## [37] "Q109244.fctrNo:Q123621.fctrNo"      
## [38] "Q109244.fctrYes:Q123621.fctrNo"     
## [39] "Q109244.fctrNA:Q123621.fctrYes"     
## [40] "Q109244.fctrNo:Q123621.fctrYes"     
## [41] "Q109244.fctrYes:Q123621.fctrYes"    
## [1] "myfit_mdl: train diagnostics complete: 5.740000 secs"

##          Prediction
## Reference    R    D
##         R 1882  209
##         D 1625  732
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.876799e-01   2.028604e-01   5.730476e-01   6.021969e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   5.298046e-15  2.076132e-239

##          Prediction
## Reference   R   D
##         R 488  38
##         D 452 142
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.625000e-01   1.596227e-01   5.328684e-01   5.918025e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   1.666221e-02   1.098739e-77 
## [1] "myfit_mdl: predict complete: 9.357000 secs"
##                                id
## 1 Interact.High.cor.Y##rcv#glmnet
##                                                                                                                                                                                  feats
## 1 Q109244.fctr,Gender.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      4.406                 0.278
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6115819    0.5939742    0.6291896       0.3369481
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6        0.672383        0.5996718
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5730476             0.6021969     0.1976596
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5920549    0.5494297    0.6346801       0.3664097
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.7       0.6657572           0.5625
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5328684             0.5918025     0.1596227
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01526771      0.03220586
## [1] "myfit_mdl: exit: 9.371000 secs"
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")
##                              label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
## 6           fit.models_0_Low.cor.X          1          5      glmnet
##       bgn     end elapsed
## 5 331.158 340.554   9.396
## 6 340.555      NA      NA
indepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.703000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0534 on full training set
## [1] "myfit_mdl: train complete: 19.985000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             88  -none-     numeric  
## beta        18656  dgCMatrix  S4       
## df             88  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         88  -none-     numeric  
## dev.ratio      88  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        212  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)                Edn.fctr^4 
##              0.1952047024             -0.1067097341 
##                Edn.fctr^6                Edn.fctr^7 
##              0.0237553435              0.0749098536 
##              Gender.fctrM             Hhold.fctrMKy 
##             -0.0703554818             -0.1490438784 
##             Hhold.fctrPKn             Hhold.fctrSKn 
##              0.5162299703              0.0279618972 
##             Hhold.fctrSKy             Income.fctr.Q 
##              0.1041010847             -0.0675479898 
##             Income.fctr.C             Income.fctr^4 
##             -0.1206129633             -0.0139722858 
##             Income.fctr^6            Q100010.fctrNo 
##              0.0007504834              0.0299022728 
##           Q100680.fctrYes           Q100689.fctrYes 
##              0.0032625549              0.1031391064 
##           Q101163.fctrDad           Q101163.fctrMom 
##             -0.0883139744              0.1099784036 
##           Q102687.fctrYes           Q103293.fctrYes 
##              0.0312745394              0.0034287002 
##            Q104996.fctrNo           Q104996.fctrYes 
##             -0.0237434125              0.0270717473 
##           Q105655.fctrYes            Q106042.fctrNo 
##             -0.0412155561             -0.0205425586 
##            Q106272.fctrNo           Q106272.fctrYes 
##              0.0175158157             -0.0284921306 
##            Q106389.fctrNo   Q106997.fctrGrrr people 
##             -0.0748652171             -0.0247097710 
##   Q106997.fctrYay people!           Q107491.fctrYes 
##              0.0764310291              0.0241770403 
##        Q108342.fctrOnline          Q108855.fctrYes! 
##              0.0659699015             -0.0544186322 
## Q108950.fctrRisk-friendly            Q109244.fctrNo 
##              0.0399793905             -0.3612442626 
##           Q109244.fctrYes           Q110740.fctrMac 
##              0.7867720593              0.0220394463 
##            Q110740.fctrPC           Q111220.fctrYes 
##             -0.0875939927              0.0991012112 
##           Q111848.fctrYes           Q112270.fctrYes 
##              0.0228026313              0.0045878812 
##            Q112478.fctrNo            Q113181.fctrNo 
##             -0.0523849496              0.1851568513 
##           Q113181.fctrYes           Q113992.fctrYes 
##             -0.1950370446              0.0126219886 
##    Q114386.fctrMysterious           Q115195.fctrYes 
##              0.0019680292              0.0030704916 
##            Q115390.fctrNo           Q115390.fctrYes 
##             -0.0745460116              0.0200284127 
##            Q115611.fctrNo           Q115611.fctrYes 
##              0.1339408085             -0.3306757390 
## Q115899.fctrCircumstances            Q115899.fctrMe 
##              0.0851135127             -0.0130795183 
##          Q116197.fctrA.M.         Q116881.fctrHappy 
##             -0.0262309208              0.0777873678 
##         Q116881.fctrRight            Q116953.fctrNo 
##             -0.1304209856             -0.0263705676 
##           Q116953.fctrYes    Q117186.fctrHot headed 
##              0.0533044251             -0.0115004864 
##      Q118232.fctrIdealist            Q118233.fctrNo 
##              0.1031325558             -0.0082146917 
##           Q118233.fctrYes        Q119650.fctrGiving 
##              0.0137212711             -0.0170457793 
##            Q119851.fctrNo           Q119851.fctrYes 
##             -0.1077857388              0.0180092566 
##           Q120012.fctrYes            Q120014.fctrNo 
##              0.0366851197              0.0336193579 
##           Q120014.fctrYes   Q120194.fctrStudy first 
##             -0.0283086949              0.0593355542 
##            Q120379.fctrNo           Q120379.fctrYes 
##             -0.0455168710              0.1013771165 
##       Q120472.fctrScience           Q120650.fctrYes 
##             -0.0264043163             -0.0258801338 
##            Q121699.fctrNo           Q121699.fctrYes 
##             -0.0654967987              0.0477679578 
##            Q121700.fctrNo           Q121700.fctrYes 
##             -0.0073966532              0.0193609196 
##           Q122120.fctrYes            Q122771.fctrPt 
##             -0.0342637586             -0.1211971800 
##            Q123464.fctrNo            Q124122.fctrNo 
##             -0.0135208179             -0.0227693557 
##            Q124742.fctrNo            YOB.Age.fctr.L 
##              0.0271760998              0.1178489426 
##            YOB.Age.fctr.Q            YOB.Age.fctr^4 
##              0.0091672083              0.0423673496 
##            YOB.Age.fctr^6            YOB.Age.fctr^7 
##              0.0067997116             -0.0389680361 
##            YOB.Age.fctr^8 
##             -0.0633534034 
## [1] "max lambda < lambdaOpt:"
##               (Intercept)                Edn.fctr^4 
##               0.192887820              -0.119849973 
##                Edn.fctr^6                Edn.fctr^7 
##               0.029850747               0.081133497 
##              Gender.fctrM             Hhold.fctrMKy 
##              -0.070364216              -0.151860222 
##             Hhold.fctrPKn             Hhold.fctrSKn 
##               0.534578849               0.033885621 
##             Hhold.fctrSKy             Income.fctr.Q 
##               0.114752162              -0.071829866 
##             Income.fctr.C             Income.fctr^4 
##              -0.130161899              -0.019455874 
##             Income.fctr^6            Q100010.fctrNo 
##               0.006025901               0.036436326 
##           Q100680.fctrYes           Q100689.fctrYes 
##               0.004776233               0.110778663 
##           Q101163.fctrDad           Q101163.fctrMom 
##              -0.093942855               0.110568655 
##           Q102687.fctrYes           Q103293.fctrYes 
##               0.035917303               0.009187328 
##            Q104996.fctrNo           Q104996.fctrYes 
##              -0.027174935               0.030840441 
##           Q105655.fctrYes            Q106042.fctrNo 
##              -0.047754402              -0.022572851 
##            Q106272.fctrNo           Q106272.fctrYes 
##               0.018750136              -0.033094547 
##            Q106389.fctrNo   Q106997.fctrGrrr people 
##              -0.081395435              -0.028816760 
##   Q106997.fctrYay people!           Q107491.fctrYes 
##               0.081972579               0.029114800 
##            Q107869.fctrNo        Q108342.fctrOnline 
##               0.002204830               0.070638186 
##          Q108855.fctrYes! Q108950.fctrRisk-friendly 
##              -0.060370840               0.045240840 
##            Q109244.fctrNo           Q109244.fctrYes 
##              -0.368068961               0.798909343 
##           Q110740.fctrMac            Q110740.fctrPC 
##               0.022194873              -0.094040223 
##           Q111220.fctrYes           Q111848.fctrYes 
##               0.105711882               0.026423743 
##           Q112270.fctrYes            Q112478.fctrNo 
##               0.011178188              -0.058869716 
##            Q113181.fctrNo           Q113181.fctrYes 
##               0.188992531              -0.200672215 
##           Q113992.fctrYes    Q114386.fctrMysterious 
##               0.018408206               0.008787278 
##           Q115195.fctrYes            Q115390.fctrNo 
##               0.006667928              -0.080608044 
##           Q115390.fctrYes            Q115611.fctrNo 
##               0.020849025               0.133196312 
##           Q115611.fctrYes Q115899.fctrCircumstances 
##              -0.339016722               0.089628166 
##            Q115899.fctrMe          Q116197.fctrA.M. 
##              -0.014103408              -0.033977326 
##         Q116881.fctrHappy         Q116881.fctrRight 
##               0.081808928              -0.134553463 
##            Q116953.fctrNo           Q116953.fctrYes 
##              -0.029073266               0.059821703 
##    Q117186.fctrHot headed      Q118232.fctrIdealist 
##              -0.017114236               0.109817378 
##            Q118233.fctrNo           Q118233.fctrYes 
##              -0.012865284               0.016738520 
##        Q119650.fctrGiving            Q119851.fctrNo 
##              -0.022919294              -0.111428922 
##           Q119851.fctrYes           Q120012.fctrYes 
##               0.019736959               0.041230245 
##            Q120014.fctrNo           Q120014.fctrYes 
##               0.037876336              -0.030635600 
##   Q120194.fctrStudy first            Q120379.fctrNo 
##               0.064690194              -0.045827828 
##           Q120379.fctrYes       Q120472.fctrScience 
##               0.108653701              -0.028048835 
##           Q120650.fctrYes            Q121699.fctrNo 
##              -0.031043954              -0.063199374 
##           Q121699.fctrYes            Q121700.fctrNo 
##               0.055354400              -0.011608644 
##           Q121700.fctrYes           Q122120.fctrYes 
##               0.019772249              -0.039779331 
##            Q122771.fctrPt            Q123464.fctrNo 
##              -0.129074066              -0.018364756 
##            Q124122.fctrNo           Q124122.fctrYes 
##              -0.026727216               0.001702308 
##            Q124742.fctrNo            YOB.Age.fctr.L 
##               0.034842422               0.133354527 
##            YOB.Age.fctr.Q            YOB.Age.fctr^4 
##               0.022697089               0.051333522 
##            YOB.Age.fctr^6            YOB.Age.fctr^7 
##               0.014400447              -0.046938593 
##            YOB.Age.fctr^8 
##              -0.071194408 
## [1] "myfit_mdl: train diagnostics complete: 20.665000 secs"

##          Prediction
## Reference    R    D
##         R 1854  237
##         D 1480  877
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.139838e-01   2.503429e-01   5.994934e-01   6.283244e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   7.745054e-30  2.178832e-197

##          Prediction
## Reference   R   D
##         R 494  32
##         D 466 128
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.553571e-01   1.476773e-01   5.256948e-01   5.847280e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   4.970426e-02   7.252253e-84 
## [1] "myfit_mdl: predict complete: 27.999000 secs"
##                      id
## 1 Low.cor.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     19.107                 1.869
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1        0.645192    0.5805835    0.7098006       0.2855263
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6       0.6835023        0.6218534
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5994934             0.6283244     0.2372918
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.6261026     0.526616    0.7255892       0.3187291
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.7       0.6648721        0.5553571
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5256948              0.584728     0.1476773
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.004944753       0.0107377
## [1] "myfit_mdl: exit: 28.013000 secs"
fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")
##                    label step_major step_minor label_minor     bgn     end
## 6 fit.models_0_Low.cor.X          1          5      glmnet 340.555 368.618
## 7       fit.models_0_end          1          6    teardown 368.619      NA
##   elapsed
## 6  28.064
## 7      NA
rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 16 fit.models          8          0           0 311.190 368.631  57.442
## 17 fit.models          8          1           1 368.632      NA      NA
fit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 372.924  NA      NA
# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
    fit.models_1_chunk_df <- 
        myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
                    major.inc = FALSE, label.minor = "setup")

    indepVar <- NULL;

    if (grepl("\\.Interact", mdl_id_pfx)) {
        if (is.null(topindep_var) && is.null(interact_vars)) {
        #   select best glmnet model upto now
            dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
                                     glb_models_df)
            dsp_models_df <- subset(dsp_models_df, 
                                    grepl(".glmnet", id, fixed = TRUE))
            bst_mdl_id <- dsp_models_df$id[1]
            mdl_id_pfx <- 
                paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
                      collapse=".")
        #   select important features
            if (is.null(bst_featsimp_df <- 
                        myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
                warning("Base model for RFE.Interact: ", bst_mdl_id, 
                        " has no important features")
                next
            }    
            
            topindep_ix <- 1
            while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
                topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
                if (grepl(".fctr", topindep_var, fixed=TRUE))
                    topindep_var <- 
                        paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
                if (topindep_var %in% names(glbFeatsInteractionOnly)) {
                    topindep_var <- NULL; topindep_ix <- topindep_ix + 1
                } else break
            }
            
        #   select features with importance > max(10, importance of .rnorm) & is not highest
        #       combine factor dummy features to just the factor feature
            if (length(pos_rnorm <- 
                       grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
                imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
                imp_rnorm <- NA    
            imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
            interact_vars <- 
                tail(row.names(subset(bst_featsimp_df, 
                                      imp > imp_cutoff)), -1)
            if (length(interact_vars) > 0) {
                interact_vars <-
                    myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
                interact_vars <- 
                    interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
            }
            ### bid0_sp only
#             interact_vars <- c(
#     "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
#     "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
#     "D.chrs.n.log", "color.fctr"
#     # , "condition.fctr", "prdl.my.descr.fctr"
#                                 )
#            interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
            ###
            indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
            indepVar <- setdiff(indepVar, topindep_var)
            if (length(interact_vars) > 0) {
                indepVar <- 
                    setdiff(indepVar, myextract_actual_feats(interact_vars))
                indepVar <- c(indepVar, 
                    paste(topindep_var, setdiff(interact_vars, topindep_var), 
                          sep = "*"))
            } else indepVar <- union(indepVar, topindep_var)
        }
    }
    
    if (is.null(indepVar))
        indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]

    if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
        indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
    
    if (is.null(indepVar))
        indepVar <- mygetIndepVar(glb_feats_df)
    
    if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {    
        indepVar <- 
            eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
    }    

    indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
    
    if (grepl("\\.Interact", mdl_id_pfx)) { 
        # if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
        if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
            if (!is.null(glbMdlFamilies[["Best.Interact"]]))
                glbMdlFamilies[[mdl_id_pfx]] <-
                    glbMdlFamilies[["Best.Interact"]]
        }
    }
    
    if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
        fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                         glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    } else fitobs_df <- glbObsFit

    if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
        mdl_methods <- glbMdlMethods else
        mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]    

    for (method in mdl_methods) {
        if (method %in% c("rpart", "rf")) {
            # rpart:    fubar's the tree
            # rf:       skip the scenario w/ .rnorm for speed
            indepVar <- setdiff(indepVar, c(".rnorm"))
            #mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
        } 

        fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, 
                            paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
                                    label.minor = method)

        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = mdl_id_pfx, 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,
            trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
            trainControl.number = glb_rcv_n_folds,
            trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = method)),
            indepVar = indepVar, rsp_var = glb_rsp_var, 
            fit_df = fitobs_df, OOB_df = glbObsOOB)
        
#         ntv_mdl <- glmnet(x = as.matrix(
#                               fitobs_df[, indepVar]), 
#                           y = as.factor(as.character(
#                               fitobs_df[, glb_rsp_var])),
#                           family = "multinomial")
#         bgn = 1; end = 100;
#         ntv_mdl <- glmnet(x = as.matrix(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]), 
#                           y = as.factor(as.character(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
#                           family = "multinomial")
    }
}
##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 372.924 372.934
## 2 fit.models_1_All.X          1          1       setup 372.935      NA
##   elapsed
## 1   0.011
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 372.935 372.943
## 3 fit.models_1_All.X          1          2      glmnet 372.944      NA
##   elapsed
## 2   0.008
## 3      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.709000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0534 on full training set
## [1] "myfit_mdl: train complete: 19.889000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             88  -none-     numeric  
## beta        18656  dgCMatrix  S4       
## df             88  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         88  -none-     numeric  
## dev.ratio      88  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        212  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)                Edn.fctr^4 
##              0.1952047024             -0.1067097341 
##                Edn.fctr^6                Edn.fctr^7 
##              0.0237553435              0.0749098536 
##              Gender.fctrM             Hhold.fctrMKy 
##             -0.0703554818             -0.1490438784 
##             Hhold.fctrPKn             Hhold.fctrSKn 
##              0.5162299703              0.0279618972 
##             Hhold.fctrSKy             Income.fctr.Q 
##              0.1041010847             -0.0675479898 
##             Income.fctr.C             Income.fctr^4 
##             -0.1206129633             -0.0139722858 
##             Income.fctr^6            Q100010.fctrNo 
##              0.0007504834              0.0299022728 
##           Q100680.fctrYes           Q100689.fctrYes 
##              0.0032625549              0.1031391064 
##           Q101163.fctrDad           Q101163.fctrMom 
##             -0.0883139744              0.1099784036 
##           Q102687.fctrYes           Q103293.fctrYes 
##              0.0312745394              0.0034287002 
##            Q104996.fctrNo           Q104996.fctrYes 
##             -0.0237434125              0.0270717473 
##           Q105655.fctrYes            Q106042.fctrNo 
##             -0.0412155561             -0.0205425586 
##            Q106272.fctrNo           Q106272.fctrYes 
##              0.0175158157             -0.0284921306 
##            Q106389.fctrNo   Q106997.fctrGrrr people 
##             -0.0748652171             -0.0247097710 
##   Q106997.fctrYay people!           Q107491.fctrYes 
##              0.0764310291              0.0241770403 
##        Q108342.fctrOnline          Q108855.fctrYes! 
##              0.0659699015             -0.0544186322 
## Q108950.fctrRisk-friendly            Q109244.fctrNo 
##              0.0399793905             -0.3612442626 
##           Q109244.fctrYes           Q110740.fctrMac 
##              0.7867720593              0.0220394463 
##            Q110740.fctrPC           Q111220.fctrYes 
##             -0.0875939927              0.0991012112 
##           Q111848.fctrYes           Q112270.fctrYes 
##              0.0228026313              0.0045878812 
##            Q112478.fctrNo            Q113181.fctrNo 
##             -0.0523849496              0.1851568513 
##           Q113181.fctrYes           Q113992.fctrYes 
##             -0.1950370446              0.0126219886 
##    Q114386.fctrMysterious           Q115195.fctrYes 
##              0.0019680292              0.0030704916 
##            Q115390.fctrNo           Q115390.fctrYes 
##             -0.0745460116              0.0200284127 
##            Q115611.fctrNo           Q115611.fctrYes 
##              0.1339408085             -0.3306757390 
## Q115899.fctrCircumstances            Q115899.fctrMe 
##              0.0851135127             -0.0130795183 
##          Q116197.fctrA.M.         Q116881.fctrHappy 
##             -0.0262309208              0.0777873678 
##         Q116881.fctrRight            Q116953.fctrNo 
##             -0.1304209856             -0.0263705676 
##           Q116953.fctrYes    Q117186.fctrHot headed 
##              0.0533044251             -0.0115004864 
##      Q118232.fctrIdealist            Q118233.fctrNo 
##              0.1031325558             -0.0082146917 
##           Q118233.fctrYes        Q119650.fctrGiving 
##              0.0137212711             -0.0170457793 
##            Q119851.fctrNo           Q119851.fctrYes 
##             -0.1077857388              0.0180092566 
##           Q120012.fctrYes            Q120014.fctrNo 
##              0.0366851197              0.0336193579 
##           Q120014.fctrYes   Q120194.fctrStudy first 
##             -0.0283086949              0.0593355542 
##            Q120379.fctrNo           Q120379.fctrYes 
##             -0.0455168710              0.1013771165 
##       Q120472.fctrScience           Q120650.fctrYes 
##             -0.0264043163             -0.0258801338 
##            Q121699.fctrNo           Q121699.fctrYes 
##             -0.0654967987              0.0477679578 
##            Q121700.fctrNo           Q121700.fctrYes 
##             -0.0073966532              0.0193609196 
##           Q122120.fctrYes            Q122771.fctrPt 
##             -0.0342637586             -0.1211971800 
##            Q123464.fctrNo            Q124122.fctrNo 
##             -0.0135208179             -0.0227693557 
##            Q124742.fctrNo            YOB.Age.fctr.L 
##              0.0271760998              0.1178489426 
##            YOB.Age.fctr.Q            YOB.Age.fctr^4 
##              0.0091672083              0.0423673496 
##            YOB.Age.fctr^6            YOB.Age.fctr^7 
##              0.0067997116             -0.0389680361 
##            YOB.Age.fctr^8 
##             -0.0633534034 
## [1] "max lambda < lambdaOpt:"
##               (Intercept)                Edn.fctr^4 
##               0.192887820              -0.119849973 
##                Edn.fctr^6                Edn.fctr^7 
##               0.029850747               0.081133497 
##              Gender.fctrM             Hhold.fctrMKy 
##              -0.070364216              -0.151860222 
##             Hhold.fctrPKn             Hhold.fctrSKn 
##               0.534578849               0.033885621 
##             Hhold.fctrSKy             Income.fctr.Q 
##               0.114752162              -0.071829866 
##             Income.fctr.C             Income.fctr^4 
##              -0.130161899              -0.019455874 
##             Income.fctr^6            Q100010.fctrNo 
##               0.006025901               0.036436326 
##           Q100680.fctrYes           Q100689.fctrYes 
##               0.004776233               0.110778663 
##           Q101163.fctrDad           Q101163.fctrMom 
##              -0.093942855               0.110568655 
##           Q102687.fctrYes           Q103293.fctrYes 
##               0.035917303               0.009187328 
##            Q104996.fctrNo           Q104996.fctrYes 
##              -0.027174935               0.030840441 
##           Q105655.fctrYes            Q106042.fctrNo 
##              -0.047754402              -0.022572851 
##            Q106272.fctrNo           Q106272.fctrYes 
##               0.018750136              -0.033094547 
##            Q106389.fctrNo   Q106997.fctrGrrr people 
##              -0.081395435              -0.028816760 
##   Q106997.fctrYay people!           Q107491.fctrYes 
##               0.081972579               0.029114800 
##            Q107869.fctrNo        Q108342.fctrOnline 
##               0.002204830               0.070638186 
##          Q108855.fctrYes! Q108950.fctrRisk-friendly 
##              -0.060370840               0.045240840 
##            Q109244.fctrNo           Q109244.fctrYes 
##              -0.368068961               0.798909343 
##           Q110740.fctrMac            Q110740.fctrPC 
##               0.022194873              -0.094040223 
##           Q111220.fctrYes           Q111848.fctrYes 
##               0.105711882               0.026423743 
##           Q112270.fctrYes            Q112478.fctrNo 
##               0.011178188              -0.058869716 
##            Q113181.fctrNo           Q113181.fctrYes 
##               0.188992531              -0.200672215 
##           Q113992.fctrYes    Q114386.fctrMysterious 
##               0.018408206               0.008787278 
##           Q115195.fctrYes            Q115390.fctrNo 
##               0.006667928              -0.080608044 
##           Q115390.fctrYes            Q115611.fctrNo 
##               0.020849025               0.133196312 
##           Q115611.fctrYes Q115899.fctrCircumstances 
##              -0.339016722               0.089628166 
##            Q115899.fctrMe          Q116197.fctrA.M. 
##              -0.014103408              -0.033977326 
##         Q116881.fctrHappy         Q116881.fctrRight 
##               0.081808928              -0.134553463 
##            Q116953.fctrNo           Q116953.fctrYes 
##              -0.029073266               0.059821703 
##    Q117186.fctrHot headed      Q118232.fctrIdealist 
##              -0.017114236               0.109817378 
##            Q118233.fctrNo           Q118233.fctrYes 
##              -0.012865284               0.016738520 
##        Q119650.fctrGiving            Q119851.fctrNo 
##              -0.022919294              -0.111428922 
##           Q119851.fctrYes           Q120012.fctrYes 
##               0.019736959               0.041230245 
##            Q120014.fctrNo           Q120014.fctrYes 
##               0.037876336              -0.030635600 
##   Q120194.fctrStudy first            Q120379.fctrNo 
##               0.064690194              -0.045827828 
##           Q120379.fctrYes       Q120472.fctrScience 
##               0.108653701              -0.028048835 
##           Q120650.fctrYes            Q121699.fctrNo 
##              -0.031043954              -0.063199374 
##           Q121699.fctrYes            Q121700.fctrNo 
##               0.055354400              -0.011608644 
##           Q121700.fctrYes           Q122120.fctrYes 
##               0.019772249              -0.039779331 
##            Q122771.fctrPt            Q123464.fctrNo 
##              -0.129074066              -0.018364756 
##            Q124122.fctrNo           Q124122.fctrYes 
##              -0.026727216               0.001702308 
##            Q124742.fctrNo            YOB.Age.fctr.L 
##               0.034842422               0.133354527 
##            YOB.Age.fctr.Q            YOB.Age.fctr^4 
##               0.022697089               0.051333522 
##            YOB.Age.fctr^6            YOB.Age.fctr^7 
##               0.014400447              -0.046938593 
##            YOB.Age.fctr^8 
##              -0.071194408 
## [1] "myfit_mdl: train diagnostics complete: 20.655000 secs"

##          Prediction
## Reference    R    D
##         R 1854  237
##         D 1480  877
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.139838e-01   2.503429e-01   5.994934e-01   6.283244e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   7.745054e-30  2.178832e-197

##          Prediction
## Reference   R   D
##         R 494  32
##         D 466 128
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.553571e-01   1.476773e-01   5.256948e-01   5.847280e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   4.970426e-02   7.252253e-84 
## [1] "myfit_mdl: predict complete: 28.143000 secs"
##                  id
## 1 All.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     19.069                 1.889
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1        0.645192    0.5805835    0.7098006       0.2855263
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6       0.6835023        0.6218534
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5994934             0.6283244     0.2372918
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.6261026     0.526616    0.7255892       0.3187291
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.7       0.6648721        0.5553571
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5256948              0.584728     0.1476773
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.004944753       0.0107377
## [1] "myfit_mdl: exit: 28.158000 secs"
##                label step_major step_minor label_minor     bgn     end
## 3 fit.models_1_All.X          1          2      glmnet 372.944 401.107
## 4 fit.models_1_All.X          1          3         glm 401.107      NA
##   elapsed
## 3  28.163
## 4      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.684000 secs"
## + Fold1.Rep1: parameter=none 
## - Fold1.Rep1: parameter=none 
## + Fold2.Rep1: parameter=none 
## - Fold2.Rep1: parameter=none 
## + Fold3.Rep1: parameter=none 
## - Fold3.Rep1: parameter=none 
## + Fold1.Rep2: parameter=none 
## - Fold1.Rep2: parameter=none 
## + Fold2.Rep2: parameter=none 
## - Fold2.Rep2: parameter=none 
## + Fold3.Rep2: parameter=none 
## - Fold3.Rep2: parameter=none 
## + Fold1.Rep3: parameter=none 
## - Fold1.Rep3: parameter=none 
## + Fold2.Rep3: parameter=none 
## - Fold2.Rep3: parameter=none 
## + Fold3.Rep3: parameter=none 
## - Fold3.Rep3: parameter=none 
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 11.877000 secs"

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5129  -1.0543   0.4371   1.0404   2.1673  
## 
## Coefficients:
##                               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                   0.404600   0.258958   1.562 0.118191    
## .rnorm                       -0.012902   0.033169  -0.389 0.697283    
## Edn.fctr.L                   -0.059377   0.153969  -0.386 0.699764    
## Edn.fctr.Q                    0.005940   0.144395   0.041 0.967187    
## Edn.fctr.C                   -0.026131   0.126132  -0.207 0.835874    
## `Edn.fctr^4`                 -0.349477   0.125171  -2.792 0.005238 ** 
## `Edn.fctr^5`                 -0.062265   0.115375  -0.540 0.589421    
## `Edn.fctr^6`                  0.125644   0.104298   1.205 0.228330    
## `Edn.fctr^7`                  0.196465   0.115276   1.704 0.088326 .  
## Gender.fctrF                 -0.384373   0.239985  -1.602 0.109232    
## Gender.fctrM                 -0.454200   0.235784  -1.926 0.054062 .  
## Hhold.fctrMKn                 0.042781   0.178827   0.239 0.810928    
## Hhold.fctrMKy                -0.136663   0.164886  -0.829 0.407196    
## Hhold.fctrPKn                 0.946054   0.253546   3.731 0.000191 ***
## Hhold.fctrPKy                 0.205624   0.336513   0.611 0.541171    
## Hhold.fctrSKn                 0.210213   0.141625   1.484 0.137732    
## Hhold.fctrSKy                 0.386839   0.243661   1.588 0.112374    
## Income.fctr.L                -0.087040   0.106772  -0.815 0.414959    
## Income.fctr.Q                -0.149860   0.097811  -1.532 0.125491    
## Income.fctr.C                -0.248123   0.095504  -2.598 0.009376 ** 
## `Income.fctr^4`              -0.103826   0.092753  -1.119 0.262977    
## `Income.fctr^5`               0.008740   0.094501   0.092 0.926312    
## `Income.fctr^6`               0.090236   0.091902   0.982 0.326165    
## Q100010.fctrNo                0.206870   0.167050   1.238 0.215577    
## Q100010.fctrYes               0.119732   0.145306   0.824 0.409942    
## Q100562.fctrNo                0.047558   0.196167   0.242 0.808441    
## Q100562.fctrYes               0.027881   0.172432   0.162 0.871549    
## Q100680.fctrNo               -0.203518   0.190446  -1.069 0.285233    
## Q100680.fctrYes              -0.182237   0.184354  -0.989 0.322900    
## Q100689.fctrNo                0.443233   0.191284   2.317 0.020496 *  
## Q100689.fctrYes               0.587654   0.189916   3.094 0.001973 ** 
## Q101162.fctrOptimist          0.012706   0.176268   0.072 0.942537    
## Q101162.fctrPessimist         0.024758   0.182085   0.136 0.891846    
## Q101163.fctrDad              -0.225265   0.156223  -1.442 0.149317    
## Q101163.fctrMom               0.080249   0.160464   0.500 0.617001    
## Q101596.fctrNo               -0.444081   0.160115  -2.774 0.005545 ** 
## Q101596.fctrYes              -0.420237   0.169044  -2.486 0.012920 *  
## Q102089.fctrOwn               0.135326   0.161333   0.839 0.401582    
## Q102089.fctrRent              0.104769   0.170542   0.614 0.538995    
## Q102289.fctrNo                0.083146   0.164966   0.504 0.614251    
## Q102289.fctrYes               0.001825   0.175851   0.010 0.991719    
## Q102674.fctrNo               -0.439035   0.213308  -2.058 0.039569 *  
## Q102674.fctrYes              -0.406335   0.224403  -1.811 0.070182 .  
## Q102687.fctrNo                0.438231   0.227210   1.929 0.053762 .  
## Q102687.fctrYes               0.491762   0.225761   2.178 0.029388 *  
## Q102906.fctrNo                0.052032   0.167165   0.311 0.755604    
## Q102906.fctrYes               0.022254   0.171654   0.130 0.896848    
## Q103293.fctrNo               -0.093048   0.150654  -0.618 0.536821    
## Q103293.fctrYes               0.034808   0.152594   0.228 0.819559    
## Q104996.fctrNo               -0.024221   0.140863  -0.172 0.863477    
## Q104996.fctrYes               0.136285   0.139161   0.979 0.327415    
## Q105655.fctrNo               -0.105306   0.171385  -0.614 0.538923    
## Q105655.fctrYes              -0.211229   0.169321  -1.248 0.212213    
## Q105840.fctrNo                0.088515   0.172747   0.512 0.608374    
## Q105840.fctrYes               0.073806   0.173692   0.425 0.670891    
## Q106042.fctrNo               -0.198059   0.171140  -1.157 0.247153    
## Q106042.fctrYes              -0.165279   0.171603  -0.963 0.335473    
## Q106272.fctrNo                0.139762   0.191733   0.729 0.466038    
## Q106272.fctrYes              -0.009718   0.178760  -0.054 0.956645    
## Q106388.fctrNo               -0.025456   0.210246  -0.121 0.903629    
## Q106388.fctrYes              -0.038330   0.222455  -0.172 0.863199    
## Q106389.fctrNo               -0.252656   0.208731  -1.210 0.226112    
## Q106389.fctrYes              -0.059304   0.210385  -0.282 0.778034    
## Q106993.fctrNo               -0.210646   0.212624  -0.991 0.321834    
## Q106993.fctrYes              -0.121628   0.188951  -0.644 0.519770    
## `Q106997.fctrGrrr people`     0.020504   0.191050   0.107 0.914535    
## `Q106997.fctrYay people!`     0.260833   0.194477   1.341 0.179854    
## Q107491.fctrNo                0.121109   0.178761   0.677 0.498097    
## Q107491.fctrYes               0.164824   0.136557   1.207 0.227434    
## Q107869.fctrNo                0.025807   0.144122   0.179 0.857886    
## Q107869.fctrYes              -0.048863   0.144799  -0.337 0.735772    
## `Q108342.fctrIn-person`       0.209445   0.172322   1.215 0.224201    
## Q108342.fctrOnline            0.335074   0.182027   1.841 0.065651 .  
## Q108343.fctrNo               -0.036563   0.179054  -0.204 0.838196    
## Q108343.fctrYes              -0.124442   0.189858  -0.655 0.512180    
## Q108617.fctrNo                0.065017   0.163619   0.397 0.691098    
## Q108617.fctrYes              -0.124073   0.203811  -0.609 0.542680    
## Q108754.fctrNo                0.047888   0.184417   0.260 0.795117    
## Q108754.fctrYes               0.053638   0.193147   0.278 0.781236    
## Q108855.fctrUmm...           -0.013429   0.208866  -0.064 0.948735    
## `Q108855.fctrYes!`           -0.158332   0.205212  -0.772 0.440379    
## Q108856.fctrSocialize        -0.210516   0.211325  -0.996 0.319167    
## Q108856.fctrSpace            -0.210701   0.197120  -1.069 0.285116    
## Q108950.fctrCautious          0.136136   0.153648   0.886 0.375605    
## `Q108950.fctrRisk-friendly`   0.255209   0.164905   1.548 0.121717    
## Q109244.fctrNo               -0.599513   0.146470  -4.093 4.26e-05 ***
## Q109244.fctrYes               0.891074   0.168470   5.289 1.23e-07 ***
## Q109367.fctrNo                0.114856   0.151809   0.757 0.449301    
## Q109367.fctrYes               0.076756   0.145361   0.528 0.597473    
## Q110740.fctrMac              -0.011357   0.128395  -0.088 0.929518    
## Q110740.fctrPC               -0.225175   0.125166  -1.799 0.072017 .  
## Q111220.fctrNo               -0.005890   0.137586  -0.043 0.965851    
## Q111220.fctrYes               0.195332   0.151003   1.294 0.195817    
## Q111580.fctrDemanding        -0.047743   0.151204  -0.316 0.752189    
## Q111580.fctrSupportive       -0.013204   0.141552  -0.093 0.925682    
## Q111848.fctrNo                0.094984   0.149118   0.637 0.524144    
## Q111848.fctrYes               0.125010   0.143627   0.870 0.384091    
## Q112270.fctrNo                0.134066   0.140609   0.953 0.340354    
## Q112270.fctrYes               0.208086   0.140614   1.480 0.138918    
## Q112478.fctrNo               -0.336368   0.171509  -1.961 0.049853 *  
## Q112478.fctrYes              -0.143906   0.165693  -0.869 0.385118    
## Q112512.fctrNo                0.050808   0.181717   0.280 0.779786    
## Q112512.fctrYes               0.008362   0.154782   0.054 0.956914    
## Q113181.fctrNo                0.234637   0.132143   1.776 0.075794 .  
## Q113181.fctrYes              -0.318203   0.135424  -2.350 0.018790 *  
## Q113583.fctrTalk              0.067798   0.194198   0.349 0.727000    
## Q113583.fctrTunes             0.099924   0.186236   0.537 0.591583    
## Q113584.fctrPeople           -0.134425   0.190832  -0.704 0.481173    
## Q113584.fctrTechnology       -0.110083   0.189503  -0.581 0.561304    
## Q113992.fctrNo                0.206162   0.153153   1.346 0.178263    
## Q113992.fctrYes               0.294459   0.164385   1.791 0.073248 .  
## Q114152.fctrNo               -0.099529   0.149933  -0.664 0.506803    
## Q114152.fctrYes              -0.019169   0.161800  -0.118 0.905694    
## Q114386.fctrMysterious        0.052465   0.151837   0.346 0.729693    
## Q114386.fctrTMI              -0.029074   0.155470  -0.187 0.851652    
## Q114517.fctrNo                0.173841   0.165377   1.051 0.293176    
## Q114517.fctrYes               0.180799   0.175543   1.030 0.303039    
## Q114748.fctrNo               -0.343176   0.175585  -1.954 0.050645 .  
## Q114748.fctrYes              -0.312489   0.173927  -1.797 0.072389 .  
## Q114961.fctrNo                0.233922   0.167844   1.394 0.163411    
## Q114961.fctrYes               0.225086   0.166736   1.350 0.177030    
## Q115195.fctrNo                0.050777   0.164871   0.308 0.758099    
## Q115195.fctrYes               0.093288   0.154971   0.602 0.547193    
## Q115390.fctrNo               -0.222548   0.148228  -1.501 0.133257    
## Q115390.fctrYes              -0.019208   0.138813  -0.138 0.889948    
## Q115602.fctrNo                0.081320   0.191123   0.425 0.670482    
## Q115602.fctrYes               0.185016   0.170758   1.083 0.278587    
## Q115610.fctrNo               -0.003961   0.201746  -0.020 0.984335    
## Q115610.fctrYes              -0.049047   0.178778  -0.274 0.783818    
## Q115611.fctrNo               -0.014545   0.187943  -0.077 0.938313    
## Q115611.fctrYes              -0.594723   0.193384  -3.075 0.002103 ** 
## Q115777.fctrEnd               0.017977   0.157850   0.114 0.909329    
## Q115777.fctrStart             0.053616   0.153909   0.348 0.727569    
## Q115899.fctrCircumstances     0.187578   0.156429   1.199 0.230477    
## Q115899.fctrMe               -0.001892   0.154074  -0.012 0.990202    
## Q116197.fctrA.M.             -0.391833   0.154428  -2.537 0.011170 *  
## Q116197.fctrP.M.             -0.278879   0.144021  -1.936 0.052821 .  
## Q116441.fctrNo               -0.160980   0.174560  -0.922 0.356423    
## Q116441.fctrYes              -0.092929   0.187661  -0.495 0.620462    
## Q116448.fctrNo                0.171616   0.165829   1.035 0.300717    
## Q116448.fctrYes               0.153260   0.167277   0.916 0.359561    
## Q116601.fctrNo                0.218695   0.193630   1.129 0.258707    
## Q116601.fctrYes               0.184643   0.165463   1.116 0.264458    
## Q116797.fctrNo               -0.156843   0.167139  -0.938 0.348037    
## Q116797.fctrYes              -0.201294   0.172288  -1.168 0.242664    
## Q116881.fctrHappy             0.142298   0.162785   0.874 0.382038    
## Q116881.fctrRight            -0.189475   0.177404  -1.068 0.285501    
## Q116953.fctrNo                0.019237   0.175674   0.110 0.912801    
## Q116953.fctrYes               0.253306   0.165211   1.533 0.125220    
## `Q117186.fctrCool headed`    -0.009769   0.163015  -0.060 0.952212    
## `Q117186.fctrHot headed`     -0.102196   0.170946  -0.598 0.549955    
## `Q117193.fctrOdd hours`       0.024491   0.159926   0.153 0.878290    
## `Q117193.fctrStandard hours` -0.050223   0.152544  -0.329 0.741978    
## Q118117.fctrNo               -0.012588   0.147559  -0.085 0.932015    
## Q118117.fctrYes               0.016563   0.149805   0.111 0.911962    
## Q118232.fctrIdealist          0.424880   0.145910   2.912 0.003592 ** 
## Q118232.fctrPragmatist        0.249776   0.144365   1.730 0.083599 .  
## Q118233.fctrNo               -0.135885   0.185318  -0.733 0.463402    
## Q118233.fctrYes               0.027417   0.200906   0.136 0.891454    
## Q118237.fctrNo               -0.182864   0.187701  -0.974 0.329941    
## Q118237.fctrYes              -0.165963   0.184848  -0.898 0.369274    
## Q118892.fctrNo                0.110529   0.131005   0.844 0.398840    
## Q118892.fctrYes               0.063150   0.123630   0.511 0.609495    
## Q119334.fctrNo               -0.121554   0.134878  -0.901 0.367474    
## Q119334.fctrYes              -0.110868   0.131794  -0.841 0.400222    
## Q119650.fctrGiving           -0.141490   0.139945  -1.011 0.311999    
## Q119650.fctrReceiving        -0.012608   0.156611  -0.081 0.935834    
## Q119851.fctrNo               -0.181962   0.161364  -1.128 0.259467    
## Q119851.fctrYes              -0.007462   0.160769  -0.046 0.962982    
## Q120012.fctrNo                0.072384   0.159828   0.453 0.650631    
## Q120012.fctrYes               0.179611   0.158737   1.132 0.257844    
## Q120014.fctrNo                0.021632   0.148794   0.145 0.884408    
## Q120014.fctrYes              -0.118054   0.141129  -0.836 0.402876    
## `Q120194.fctrStudy first`     0.275430   0.136862   2.012 0.044171 *  
## `Q120194.fctrTry first`       0.180054   0.142161   1.267 0.205317    
## Q120379.fctrNo               -0.051802   0.150377  -0.344 0.730485    
## Q120379.fctrYes               0.211667   0.149287   1.418 0.156234    
## Q120472.fctrArt              -0.051686   0.153358  -0.337 0.736097    
## Q120472.fctrScience          -0.111310   0.143161  -0.778 0.436855    
## Q120650.fctrNo               -0.085901   0.194182  -0.442 0.658217    
## Q120650.fctrYes              -0.212186   0.142711  -1.487 0.137060    
## Q120978.fctrNo                0.062449   0.155800   0.401 0.688547    
## Q120978.fctrYes               0.070923   0.152319   0.466 0.641485    
## Q121011.fctrNo                0.184986   0.156694   1.181 0.237780    
## Q121011.fctrYes               0.141255   0.154185   0.916 0.359596    
## Q121699.fctrNo                0.377420   0.238968   1.579 0.114251    
## Q121699.fctrYes               0.573505   0.230376   2.489 0.012795 *  
## Q121700.fctrNo               -0.471980   0.232495  -2.030 0.042350 *  
## Q121700.fctrYes              -0.381078   0.251280  -1.517 0.129380    
## Q122120.fctrNo               -0.041853   0.137012  -0.305 0.760008    
## Q122120.fctrYes              -0.153346   0.150608  -1.018 0.308592    
## Q122769.fctrNo               -0.067759   0.206885  -0.328 0.743275    
## Q122769.fctrYes              -0.083726   0.209971  -0.399 0.690076    
## Q122770.fctrNo                0.168409   0.253192   0.665 0.505958    
## Q122770.fctrYes               0.153623   0.250000   0.614 0.538891    
## Q122771.fctrPc               -0.157444   0.232168  -0.678 0.497680    
## Q122771.fctrPt               -0.386119   0.246090  -1.569 0.116644    
## Q123464.fctrNo               -0.052050   0.157938  -0.330 0.741733    
## Q123464.fctrYes               0.112459   0.230993   0.487 0.626364    
## Q123621.fctrNo               -0.080284   0.163157  -0.492 0.622674    
## Q123621.fctrYes              -0.062375   0.167728  -0.372 0.709981    
## Q124122.fctrNo               -0.042217   0.134780  -0.313 0.754107    
## Q124122.fctrYes               0.085903   0.129422   0.664 0.506856    
## Q124742.fctrNo                0.156242   0.103466   1.510 0.131024    
## Q124742.fctrYes               0.009912   0.119612   0.083 0.933959    
## YOB.Age.fctr.L                0.474767   0.188127   2.524 0.011614 *  
## YOB.Age.fctr.Q                0.229265   0.156292   1.467 0.142403    
## YOB.Age.fctr.C               -0.057252   0.135106  -0.424 0.671743    
## `YOB.Age.fctr^4`              0.211239   0.126263   1.673 0.094326 .  
## `YOB.Age.fctr^5`              0.075605   0.116986   0.646 0.518104    
## `YOB.Age.fctr^6`              0.133065   0.105371   1.263 0.206653    
## `YOB.Age.fctr^7`             -0.175589   0.099829  -1.759 0.078595 .  
## `YOB.Age.fctr^8`             -0.209542   0.102857  -2.037 0.041628 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6150.3  on 4447  degrees of freedom
## Residual deviance: 5378.0  on 4235  degrees of freedom
## AIC: 5804
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "myfit_mdl: train diagnostics complete: 13.658000 secs"

##          Prediction
## Reference    R    D
##         R 1719  372
##         D 1198 1159
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.470324e-01   3.069788e-01   6.327828e-01   6.610886e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   1.956853e-56   2.785875e-96

##          Prediction
## Reference   R   D
##         R 457  69
##         D 396 198
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.848214e-01   1.952083e-01   5.553253e-01   6.138708e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   1.400930e-04   1.234384e-51 
## [1] "myfit_mdl: predict complete: 21.259000 secs"
##               id
## 1 All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                     11.096                 1.313
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6722741    0.6432329    0.7013152       0.2700242
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6       0.6865016        0.5999708
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6327828             0.6610886     0.1961948
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5998195    0.5380228    0.6616162       0.3425094
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.7       0.6627991        0.5848214
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5553253             0.6138708     0.1952083
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.006222678      0.01300276
## [1] "myfit_mdl: exit: 21.273000 secs"
# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
                label.minor = "preProc")
##                  label step_major step_minor label_minor     bgn     end
## 4   fit.models_1_All.X          1          3         glm 401.107 422.431
## 5 fit.models_1_preProc          1          4     preProc 422.432      NA
##   elapsed
## 4  21.324
## 5      NA
mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
                                                      "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
    fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                     glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    
} else fitobs_df <- glbObsFit

for (prePr in glb_preproc_methods) {   
    # The operations are applied in this order: 
    #   Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
    
    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
            id.prefix=mdl_id_pfx, 
            type=glb_model_type, tune.df=glbMdlTuneParams,
            trainControl.method="repeatedcv",
            trainControl.number=glb_rcv_n_folds,
            trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method=method, train.preProcess=prePr)),
            indepVar=indepVar, rsp_var=glb_rsp_var, 
            fit_df=fitobs_df, OOB_df=glbObsOOB)
}            
    
    # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
    #   check NA coefficients & filter appropriate terms in indepVar
#     if (method == "glm") {
#         orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
#         orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
#         orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
#           require(car)
#           vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
#           # if vif errors out with "there are aliased coefficients in the model"
#               alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
#           print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
#           print(which.max(vif_orig_glm))
#           print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
#           glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
#           glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in%    grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
#           all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
#           cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
#           mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
#           subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
#         corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
#           which.max(abs_corxx_mtrx["S.T.tribun", ])
#           abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
#         step_glm <- step(orig_glm)
#     }
    # Since caret does not optimize rpart well
#     if (method == "rpart")
#         ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
#                                 indepVar=indepVar,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,        
#             n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))

# User specified
#   Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df

    # easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
#                         , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")

    # easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
#     ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
#     ,"prdline.my.fctr*biddable"
#     #,"prdline.my.fctr*startprice.log"
#     #,"prdline.my.fctr*startprice.diff"    
#     ,"prdline.my.fctr*condition.fctr"
#     ,"prdline.my.fctr*D.terms.post.stop.n"
#     #,"prdline.my.fctr*D.terms.post.stem.n"
#     ,"prdline.my.fctr*cellular.fctr"    
# #    ,"<feat1>:<feat2>"
#                                            )
# for (method in glbMdlMethods) {
#     ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
#                                 indepVar=indepVar,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,
#                     n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
#     csm_mdl_id <- paste0(mdl_id, ".", method)
#     csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
#                                                                      method)]]);               print(head(csm_featsimp_df))
# }
###

# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))

#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]

    # User specified bivariate models
#     indepVar_lst <- list()
#     for (feat in setdiff(names(glbObsFit), 
#                          union(glb_rsp_var, glbFeatsExclude)))
#         indepVar_lst[["feat"]] <- feat

    # User specified combinatorial models
#     indepVar_lst <- list()
#     combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"), 
#                           <num_feats_to_choose>)
#     for (combn_ix in 1:ncol(combn_mtrx))
#         #print(combn_mtrx[, combn_ix])
#         indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
    
    # template for myfit_mdl
    #   rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
    #       only for OOB in trainControl ?
    
#     ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
#                             indepVar=indepVar,
#                             rsp_var=glb_rsp_var,
#                             fit_df=glbObsFit, OOB_df=glbObsOOB,
#                             n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
#                             model_loss_mtrx=glbMdlMetric_terms,
#                             model_summaryFunction=glbMdlMetricSummaryFn,
#                             model_metric=glbMdlMetricSummary,
#                             model_metric_maximize=glbMdlMetricMaximize)

# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)

# Non-caret models
#     rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var), 
#                                data=glbObsFit, #method="class", 
#                                control=rpart.control(cp=0.12),
#                            parms=list(loss=glbMdlMetric_terms))
#     print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
# 

print(glb_models_df)
##                                                              id
## MFO###myMFO_classfr                         MFO###myMFO_classfr
## Random###myrandom_classfr             Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart                       Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          feats
## MFO###myMFO_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     .rnorm
## Random###myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               .rnorm
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Q109244.fctr,Gender.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## Low.cor.X##rcv#glmnet           Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glmnet               Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glm                  Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##                                 max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr                           0                      0.429
## Random###myrandom_classfr                     0                      0.346
## Max.cor.Y.rcv.1X1###glmnet                    0                      0.907
## Max.cor.Y##rcv#rpart                          5                      1.862
## Interact.High.cor.Y##rcv#glmnet              25                      4.406
## Low.cor.X##rcv#glmnet                        25                     19.107
## All.X##rcv#glmnet                            25                     19.069
## All.X##rcv#glm                                1                     11.096
##                                 min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr                             0.003       0.5000000
## Random###myrandom_classfr                       0.002       0.4942483
## Max.cor.Y.rcv.1X1###glmnet                      0.064       0.5971118
## Max.cor.Y##rcv#rpart                            0.019       0.5971118
## Interact.High.cor.Y##rcv#glmnet                 0.278       0.6115819
## Low.cor.X##rcv#glmnet                           1.869       0.6451920
## All.X##rcv#glmnet                               1.889       0.6451920
## All.X##rcv#glm                                  1.313       0.6722741
##                                 max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr                0.0000000    1.0000000       0.5000000
## Random###myrandom_classfr          0.4619799    0.5265168       0.5073101
## Max.cor.Y.rcv.1X1###glmnet         0.5480631    0.6461604       0.3580613
## Max.cor.Y##rcv#rpart               0.5480631    0.6461604       0.3676308
## Interact.High.cor.Y##rcv#glmnet    0.5939742    0.6291896       0.3369481
## Low.cor.X##rcv#glmnet              0.5805835    0.7098006       0.2855263
## All.X##rcv#glmnet                  0.5805835    0.7098006       0.2855263
## All.X##rcv#glm                     0.6432329    0.7013152       0.2700242
##                                 opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr                                0.5       0.6395473
## Random###myrandom_classfr                          0.6       0.6395473
## Max.cor.Y.rcv.1X1###glmnet                         0.6       0.6720662
## Max.cor.Y##rcv#rpart                               0.6       0.6720662
## Interact.High.cor.Y##rcv#glmnet                    0.6       0.6723830
## Low.cor.X##rcv#glmnet                              0.6       0.6835023
## All.X##rcv#glmnet                                  0.6       0.6835023
## All.X##rcv#glm                                     0.6       0.6865016
##                                 max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr                    0.4700989             0.4553427
## Random###myrandom_classfr              0.4700989             0.4553427
## Max.cor.Y.rcv.1X1###glmnet             0.5721673             0.5574714
## Max.cor.Y##rcv#rpart                   0.6000450             0.5574714
## Interact.High.cor.Y##rcv#glmnet        0.5996718             0.5730476
## Low.cor.X##rcv#glmnet                  0.6218534             0.5994934
## All.X##rcv#glmnet                      0.6218534             0.5994934
## All.X##rcv#glm                         0.5999708             0.6327828
##                                 max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr                         0.4848945     0.0000000
## Random###myrandom_classfr                   0.4848945     0.0000000
## Max.cor.Y.rcv.1X1###glmnet                  0.5867683     0.1772539
## Max.cor.Y##rcv#rpart                        0.5867683     0.1947896
## Interact.High.cor.Y##rcv#glmnet             0.6021969     0.1976596
## Low.cor.X##rcv#glmnet                       0.6283244     0.2372918
## All.X##rcv#glmnet                           0.6283244     0.2372918
## All.X##rcv#glm                              0.6610886     0.1961948
##                                 max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr                   0.5000000    0.0000000    1.0000000
## Random###myrandom_classfr             0.5235690    0.5000000    0.5471380
## Max.cor.Y.rcv.1X1###glmnet            0.5896897    0.5228137    0.6565657
## Max.cor.Y##rcv#rpart                  0.5896897    0.5228137    0.6565657
## Interact.High.cor.Y##rcv#glmnet       0.5920549    0.5494297    0.6346801
## Low.cor.X##rcv#glmnet                 0.6261026    0.5266160    0.7255892
## All.X##rcv#glmnet                     0.6261026    0.5266160    0.7255892
## All.X##rcv#glm                        0.5998195    0.5380228    0.6616162
##                                 max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr                   0.5000000                    0.5
## Random###myrandom_classfr             0.5191202                    0.6
## Max.cor.Y.rcv.1X1###glmnet            0.3658672                    0.6
## Max.cor.Y##rcv#rpart                  0.3774772                    0.6
## Interact.High.cor.Y##rcv#glmnet       0.3664097                    0.7
## Low.cor.X##rcv#glmnet                 0.3187291                    0.7
## All.X##rcv#glmnet                     0.3187291                    0.7
## All.X##rcv#glm                        0.3425094                    0.7
##                                 max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr                   0.6391252        0.4696429
## Random###myrandom_classfr             0.6391252        0.4696429
## Max.cor.Y.rcv.1X1###glmnet            0.6643789        0.5633929
## Max.cor.Y##rcv#rpart                  0.6643789        0.5633929
## Interact.High.cor.Y##rcv#glmnet       0.6657572        0.5625000
## Low.cor.X##rcv#glmnet                 0.6648721        0.5553571
## All.X##rcv#glmnet                     0.6648721        0.5553571
## All.X##rcv#glm                        0.6627991        0.5848214
##                                 max.AccuracyLower.OOB
## MFO###myMFO_classfr                         0.4400805
## Random###myrandom_classfr                   0.4400805
## Max.cor.Y.rcv.1X1###glmnet                  0.5337655
## Max.cor.Y##rcv#rpart                        0.5337655
## Interact.High.cor.Y##rcv#glmnet             0.5328684
## Low.cor.X##rcv#glmnet                       0.5256948
## All.X##rcv#glmnet                           0.5256948
## All.X##rcv#glm                              0.5553253
##                                 max.AccuracyUpper.OOB max.Kappa.OOB
## MFO###myMFO_classfr                         0.4993651     0.0000000
## Random###myrandom_classfr                   0.4993651     0.0000000
## Max.cor.Y.rcv.1X1###glmnet                  0.5926864     0.1605510
## Max.cor.Y##rcv#rpart                        0.5926864     0.1605510
## Interact.High.cor.Y##rcv#glmnet             0.5918025     0.1596227
## Low.cor.X##rcv#glmnet                       0.5847280     0.1476773
## All.X##rcv#glmnet                           0.5847280     0.1476773
## All.X##rcv#glm                              0.6138708     0.1952083
##                                 max.AccuracySD.fit max.KappaSD.fit
## MFO###myMFO_classfr                             NA              NA
## Random###myrandom_classfr                       NA              NA
## Max.cor.Y.rcv.1X1###glmnet                      NA              NA
## Max.cor.Y##rcv#rpart                   0.012403504      0.02559319
## Interact.High.cor.Y##rcv#glmnet        0.015267709      0.03220586
## Low.cor.X##rcv#glmnet                  0.004944753      0.01073770
## All.X##rcv#glmnet                      0.004944753      0.01073770
## All.X##rcv#glm                         0.006222678      0.01300276
rm(ret_lst)
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
                label.minor = "teardown")
##                  label step_major step_minor label_minor     bgn     end
## 5 fit.models_1_preProc          1          4     preProc 422.432 422.503
## 6     fit.models_1_end          1          5    teardown 422.503      NA
##   elapsed
## 5   0.071
## 6      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 17 fit.models          8          1           1 368.632 422.512   53.88
## 18 fit.models          8          2           2 422.512      NA      NA
fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 426.412  NA      NA
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
    plt_models_df[, sub("min.", "inv.", var)] <- 
        #ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
        1.0 / plt_models_df[, var]
    plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)
##                                                              id
## MFO###myMFO_classfr                         MFO###myMFO_classfr
## Random###myrandom_classfr             Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart                       Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          feats
## MFO###myMFO_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     .rnorm
## Random###myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               .rnorm
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Q109244.fctr,Gender.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## Low.cor.X##rcv#glmnet           Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glmnet               Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glm                  Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##                                 max.nTuningRuns max.AUCpROC.fit
## MFO###myMFO_classfr                           0       0.5000000
## Random###myrandom_classfr                     0       0.4942483
## Max.cor.Y.rcv.1X1###glmnet                    0       0.5971118
## Max.cor.Y##rcv#rpart                          5       0.5971118
## Interact.High.cor.Y##rcv#glmnet              25       0.6115819
## Low.cor.X##rcv#glmnet                        25       0.6451920
## All.X##rcv#glmnet                            25       0.6451920
## All.X##rcv#glm                                1       0.6722741
##                                 max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr                0.0000000    1.0000000       0.5000000
## Random###myrandom_classfr          0.4619799    0.5265168       0.5073101
## Max.cor.Y.rcv.1X1###glmnet         0.5480631    0.6461604       0.3580613
## Max.cor.Y##rcv#rpart               0.5480631    0.6461604       0.3676308
## Interact.High.cor.Y##rcv#glmnet    0.5939742    0.6291896       0.3369481
## Low.cor.X##rcv#glmnet              0.5805835    0.7098006       0.2855263
## All.X##rcv#glmnet                  0.5805835    0.7098006       0.2855263
## All.X##rcv#glm                     0.6432329    0.7013152       0.2700242
##                                 opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr                                0.5       0.6395473
## Random###myrandom_classfr                          0.6       0.6395473
## Max.cor.Y.rcv.1X1###glmnet                         0.6       0.6720662
## Max.cor.Y##rcv#rpart                               0.6       0.6720662
## Interact.High.cor.Y##rcv#glmnet                    0.6       0.6723830
## Low.cor.X##rcv#glmnet                              0.6       0.6835023
## All.X##rcv#glmnet                                  0.6       0.6835023
## All.X##rcv#glm                                     0.6       0.6865016
##                                 max.Accuracy.fit max.Kappa.fit
## MFO###myMFO_classfr                    0.4700989     0.0000000
## Random###myrandom_classfr              0.4700989     0.0000000
## Max.cor.Y.rcv.1X1###glmnet             0.5721673     0.1772539
## Max.cor.Y##rcv#rpart                   0.6000450     0.1947896
## Interact.High.cor.Y##rcv#glmnet        0.5996718     0.1976596
## Low.cor.X##rcv#glmnet                  0.6218534     0.2372918
## All.X##rcv#glmnet                      0.6218534     0.2372918
## All.X##rcv#glm                         0.5999708     0.1961948
##                                 max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr                   0.5000000    0.0000000    1.0000000
## Random###myrandom_classfr             0.5235690    0.5000000    0.5471380
## Max.cor.Y.rcv.1X1###glmnet            0.5896897    0.5228137    0.6565657
## Max.cor.Y##rcv#rpart                  0.5896897    0.5228137    0.6565657
## Interact.High.cor.Y##rcv#glmnet       0.5920549    0.5494297    0.6346801
## Low.cor.X##rcv#glmnet                 0.6261026    0.5266160    0.7255892
## All.X##rcv#glmnet                     0.6261026    0.5266160    0.7255892
## All.X##rcv#glm                        0.5998195    0.5380228    0.6616162
##                                 max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr                   0.5000000                    0.5
## Random###myrandom_classfr             0.5191202                    0.6
## Max.cor.Y.rcv.1X1###glmnet            0.3658672                    0.6
## Max.cor.Y##rcv#rpart                  0.3774772                    0.6
## Interact.High.cor.Y##rcv#glmnet       0.3664097                    0.7
## Low.cor.X##rcv#glmnet                 0.3187291                    0.7
## All.X##rcv#glmnet                     0.3187291                    0.7
## All.X##rcv#glm                        0.3425094                    0.7
##                                 max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr                   0.6391252        0.4696429
## Random###myrandom_classfr             0.6391252        0.4696429
## Max.cor.Y.rcv.1X1###glmnet            0.6643789        0.5633929
## Max.cor.Y##rcv#rpart                  0.6643789        0.5633929
## Interact.High.cor.Y##rcv#glmnet       0.6657572        0.5625000
## Low.cor.X##rcv#glmnet                 0.6648721        0.5553571
## All.X##rcv#glmnet                     0.6648721        0.5553571
## All.X##rcv#glm                        0.6627991        0.5848214
##                                 max.Kappa.OOB inv.elapsedtime.everything
## MFO###myMFO_classfr                 0.0000000                 2.33100233
## Random###myrandom_classfr           0.0000000                 2.89017341
## Max.cor.Y.rcv.1X1###glmnet          0.1605510                 1.10253583
## Max.cor.Y##rcv#rpart                0.1605510                 0.53705693
## Interact.High.cor.Y##rcv#glmnet     0.1596227                 0.22696323
## Low.cor.X##rcv#glmnet               0.1476773                 0.05233684
## All.X##rcv#glmnet                   0.1476773                 0.05244113
## All.X##rcv#glm                      0.1952083                 0.09012257
##                                 inv.elapsedtime.final
## MFO###myMFO_classfr                       333.3333333
## Random###myrandom_classfr                 500.0000000
## Max.cor.Y.rcv.1X1###glmnet                 15.6250000
## Max.cor.Y##rcv#rpart                       52.6315789
## Interact.High.cor.Y##rcv#glmnet             3.5971223
## Low.cor.X##rcv#glmnet                       0.5350455
## All.X##rcv#glmnet                           0.5293806
## All.X##rcv#glm                              0.7616146
# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df, 
#         !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))

# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df, 
                max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
                min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
    # Does CI alredy exist ?
    var_components <- unlist(strsplit(var, "SD"))
    varActul <- paste0(var_components[1],          var_components[2])
    varUpper <- paste0(var_components[1], "Upper", var_components[2])
    varLower <- paste0(var_components[1], "Lower", var_components[2])
    if (varUpper %in% names(glb_models_df)) {
        warning(varUpper, " already exists in glb_models_df")
        # Assuming Lower also exists
        next
    }    
    print(sprintf("var:%s", var))
    # CI is dependent on sample size in t distribution; df=n-1
    glb_models_df[, varUpper] <- glb_models_df[, varActul] + 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
    glb_models_df[, varLower] <- glb_models_df[, varActul] - 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
    var_components <- unlist(strsplit(var, "Upper"))
    col_name <- unlist(paste(var_components, collapse=""))
    plt_models_df[, col_name] <- glb_models_df[, col_name]
    for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
        pltCI_models_df[, name] <- glb_models_df[, name]
}

build_statsCI_data <- function(plt_models_df) {
    mltd_models_df <- melt(plt_models_df, id.vars="id")
    mltd_models_df$data <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) tail(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), "[.]")), 1))
    mltd_models_df$label <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) head(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), 
            paste0(".", mltd_models_df[row_ix, "data"]))), 1))
    #print(mltd_models_df)
    
    return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)

mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
    for (type in c("Upper", "Lower")) {
        if (length(var_components <- unlist(strsplit(
                as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
            #print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
            mltdCI_models_df[row_ix, "label"] <- var_components[1]
            mltdCI_models_df[row_ix, "data"] <- 
                unlist(strsplit(var_components[2], "[.]"))[2]
            mltdCI_models_df[row_ix, "type"] <- type
            break
        }
    }    
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable), 
                            timevar="type", 
        idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")), 
                            direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)

# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
    for (type in unique(mltd_models_df$data)) {
        var_type <- paste0(var, ".", type)
        # if this data is already present, next
        if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
                                       sep=".")))
            next
        #print(sprintf("var_type:%s", var_type))
        goback_vars <- c(goback_vars, var_type)
    }
}

if (length(goback_vars) > 0) {
    mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
    mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}

# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")], 
#                         all.x=TRUE)

png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") + 
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") + 
        geom_errorbar(data=mrgdCI_models_df, 
            mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
          facet_grid(label ~ data, scales="free") + 
          theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
## Warning: Removed 4 rows containing missing values (geom_errorbar).
dev.off()
## quartz_off_screen 
##                 2
print(gp)
## Warning: Removed 4 rows containing missing values (geom_errorbar).

dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
# if (glb_is_classification && glb_is_binomial) 
#     dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])
##                                id max.Accuracy.OOB max.AUCROCR.OOB
## 8                  All.X##rcv#glm        0.5848214       0.3425094
## 4            Max.cor.Y##rcv#rpart        0.5633929       0.3774772
## 3      Max.cor.Y.rcv.1X1###glmnet        0.5633929       0.3658672
## 5 Interact.High.cor.Y##rcv#glmnet        0.5625000       0.3664097
## 6           Low.cor.X##rcv#glmnet        0.5553571       0.3187291
## 7               All.X##rcv#glmnet        0.5553571       0.3187291
## 2       Random###myrandom_classfr        0.4696429       0.5191202
## 1             MFO###myMFO_classfr        0.4696429       0.5000000
##   max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 8       0.5998195        0.5999708                    0.6
## 4       0.5896897        0.6000450                    0.6
## 3       0.5896897        0.5721673                    0.6
## 5       0.5920549        0.5996718                    0.6
## 6       0.6261026        0.6218534                    0.6
## 7       0.6261026        0.6218534                    0.6
## 2       0.5235690        0.4700989                    0.6
## 1       0.5000000        0.4700989                    0.5
##   opt.prob.threshold.OOB
## 8                    0.7
## 4                    0.6
## 3                    0.6
## 5                    0.7
## 6                    0.7
## 7                    0.7
## 2                    0.6
## 1                    0.5
# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit - 
##     opt.prob.threshold.OOB
## <environment: 0x7fb10c14a4b0>
print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))
## [1] "Best model id: All.X##rcv#glm"
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df    

myget_category_stats <- function(obs_df, mdl_id, label) {
    require(dplyr)
    require(lazyeval)
    
    predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value        
    predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
    
    if (!predct_var_name %in% names(obs_df))
        obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
    
    tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var, 
                             predct_var_name, predct_error_var_name)]
#     tmp_obs_df <- obs_df %>%
#         dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name) 
    #dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
    names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
    
    ret_ctgry_df <- tmp_obs_df %>%
        dplyr::group_by_(glbFeatsCategory) %>%
        dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)), 
            interp(~sum(var), var=as.name(paste0("err.abs.", label))), 
            interp(~mean(var), var=as.name(paste0("err.abs.", label))),
            interp(~n()))
    names(ret_ctgry_df) <- c(glbFeatsCategory, 
                             #paste0(glb_rsp_var, ".abs.", label, ".sum"),
                             paste0("err.abs.", label, ".sum"),                             
                             paste0("err.abs.", label, ".mean"), 
                             paste0(".n.", label))
    ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
    #colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
    
    return(ret_ctgry_df)    
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))

if (!is.null(glb_mdl_ensemble)) {
    fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df, 
                            paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE, 
                                                label.minor = "ensemble")
    
    mdl_id_pfx <- "Ensemble"

    if (#(glb_is_regression) | 
        ((glb_is_classification) & (!glb_is_binomial)))
        stop("Ensemble models not implemented yet for multinomial classification")
    
    mygetEnsembleAutoMdlIds <- function() {
        tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
        row.names(tmp_models_df) <- tmp_models_df$id
        mdl_threshold_pos <- 
            min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
        mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
        return(mdlIds[!grepl("Ensemble", mdlIds)])
    }
    
    if (glb_mdl_ensemble == "auto") {
        glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
        mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")        
    } else if (grepl("^%<d-%", glb_mdl_ensemble)) {
        glb_mdl_ensemble <- eval(parse(text =
                        str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
    }
    
    for (mdl_id in glb_mdl_ensemble) {
        if (!(mdl_id %in% names(glb_models_lst))) {
            warning("Model ", mdl_id, " in glb_model_ensemble not found !")
            next
        }
        glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
        glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
    }
    
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
    
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
    
    ### bid0_sp
    #  Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
    #  old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
    #  RFE only ;       models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
    #  RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
    #  RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
    #  RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
    #  RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
    #  RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    #  RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    ### bid0_sp
    ### bid1_sp
    # "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
    # "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
    ### bid1_sp

    indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
    if (glb_is_classification)
        indepVar <- paste(indepVar, ".prob", sep = "")
    # Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
    indepVar <- intersect(indepVar, names(glbObsFit))
    
#     indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
#     if (glb_is_regression)
#         indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
#     if (glb_is_classification && glb_is_binomial)
#         indepVar <- grep("prob$", indepVar, value=TRUE) else
#         indepVar <- indepVar[!grepl("err$", indepVar)]

    #rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
    
    for (method in c("glm", "glmnet")) {
        for (trainControlMethod in 
             c("boot", "boot632", "cv", "repeatedcv"
               #, "LOOCV" # tuneLength * nrow(fitDF)
               , "LGOCV", "adaptive_cv"
               #, "adaptive_boot"  #error: adaptive$min should be less than 3 
               #, "adaptive_LGOCV" #error: adaptive$min should be less than 3 
               )) {
            #sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
            #glb_models_df <- sav_models_df; print(glb_models_df$id)
                
            if ((method == "glm") && (trainControlMethod != "repeatedcv"))
                # glm used only to identify outliers
                next
            
            ret_lst <- myfit_mdl(
                mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod), 
                    type = glb_model_type, tune.df = NULL,
                    trainControl.method = trainControlMethod,
                    trainControl.number = glb_rcv_n_folds,
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    }
    dsp_models_df <- get_dsp_models_df()
}

if (is.null(glbMdlSelId)) 
    glbMdlSelId <- dsp_models_df[1, "id"] else 
    print(sprintf("User specified selection: %s", glbMdlSelId))   
## [1] "User specified selection: All.X##rcv#glmnet"
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])

##             Length Class      Mode     
## a0             88  -none-     numeric  
## beta        18656  dgCMatrix  S4       
## df             88  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         88  -none-     numeric  
## dev.ratio      88  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        212  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)                Edn.fctr^4 
##              0.1952047024             -0.1067097341 
##                Edn.fctr^6                Edn.fctr^7 
##              0.0237553435              0.0749098536 
##              Gender.fctrM             Hhold.fctrMKy 
##             -0.0703554818             -0.1490438784 
##             Hhold.fctrPKn             Hhold.fctrSKn 
##              0.5162299703              0.0279618972 
##             Hhold.fctrSKy             Income.fctr.Q 
##              0.1041010847             -0.0675479898 
##             Income.fctr.C             Income.fctr^4 
##             -0.1206129633             -0.0139722858 
##             Income.fctr^6            Q100010.fctrNo 
##              0.0007504834              0.0299022728 
##           Q100680.fctrYes           Q100689.fctrYes 
##              0.0032625549              0.1031391064 
##           Q101163.fctrDad           Q101163.fctrMom 
##             -0.0883139744              0.1099784036 
##           Q102687.fctrYes           Q103293.fctrYes 
##              0.0312745394              0.0034287002 
##            Q104996.fctrNo           Q104996.fctrYes 
##             -0.0237434125              0.0270717473 
##           Q105655.fctrYes            Q106042.fctrNo 
##             -0.0412155561             -0.0205425586 
##            Q106272.fctrNo           Q106272.fctrYes 
##              0.0175158157             -0.0284921306 
##            Q106389.fctrNo   Q106997.fctrGrrr people 
##             -0.0748652171             -0.0247097710 
##   Q106997.fctrYay people!           Q107491.fctrYes 
##              0.0764310291              0.0241770403 
##        Q108342.fctrOnline          Q108855.fctrYes! 
##              0.0659699015             -0.0544186322 
## Q108950.fctrRisk-friendly            Q109244.fctrNo 
##              0.0399793905             -0.3612442626 
##           Q109244.fctrYes           Q110740.fctrMac 
##              0.7867720593              0.0220394463 
##            Q110740.fctrPC           Q111220.fctrYes 
##             -0.0875939927              0.0991012112 
##           Q111848.fctrYes           Q112270.fctrYes 
##              0.0228026313              0.0045878812 
##            Q112478.fctrNo            Q113181.fctrNo 
##             -0.0523849496              0.1851568513 
##           Q113181.fctrYes           Q113992.fctrYes 
##             -0.1950370446              0.0126219886 
##    Q114386.fctrMysterious           Q115195.fctrYes 
##              0.0019680292              0.0030704916 
##            Q115390.fctrNo           Q115390.fctrYes 
##             -0.0745460116              0.0200284127 
##            Q115611.fctrNo           Q115611.fctrYes 
##              0.1339408085             -0.3306757390 
## Q115899.fctrCircumstances            Q115899.fctrMe 
##              0.0851135127             -0.0130795183 
##          Q116197.fctrA.M.         Q116881.fctrHappy 
##             -0.0262309208              0.0777873678 
##         Q116881.fctrRight            Q116953.fctrNo 
##             -0.1304209856             -0.0263705676 
##           Q116953.fctrYes    Q117186.fctrHot headed 
##              0.0533044251             -0.0115004864 
##      Q118232.fctrIdealist            Q118233.fctrNo 
##              0.1031325558             -0.0082146917 
##           Q118233.fctrYes        Q119650.fctrGiving 
##              0.0137212711             -0.0170457793 
##            Q119851.fctrNo           Q119851.fctrYes 
##             -0.1077857388              0.0180092566 
##           Q120012.fctrYes            Q120014.fctrNo 
##              0.0366851197              0.0336193579 
##           Q120014.fctrYes   Q120194.fctrStudy first 
##             -0.0283086949              0.0593355542 
##            Q120379.fctrNo           Q120379.fctrYes 
##             -0.0455168710              0.1013771165 
##       Q120472.fctrScience           Q120650.fctrYes 
##             -0.0264043163             -0.0258801338 
##            Q121699.fctrNo           Q121699.fctrYes 
##             -0.0654967987              0.0477679578 
##            Q121700.fctrNo           Q121700.fctrYes 
##             -0.0073966532              0.0193609196 
##           Q122120.fctrYes            Q122771.fctrPt 
##             -0.0342637586             -0.1211971800 
##            Q123464.fctrNo            Q124122.fctrNo 
##             -0.0135208179             -0.0227693557 
##            Q124742.fctrNo            YOB.Age.fctr.L 
##              0.0271760998              0.1178489426 
##            YOB.Age.fctr.Q            YOB.Age.fctr^4 
##              0.0091672083              0.0423673496 
##            YOB.Age.fctr^6            YOB.Age.fctr^7 
##              0.0067997116             -0.0389680361 
##            YOB.Age.fctr^8 
##             -0.0633534034 
## [1] "max lambda < lambdaOpt:"
##               (Intercept)                Edn.fctr^4 
##               0.192887820              -0.119849973 
##                Edn.fctr^6                Edn.fctr^7 
##               0.029850747               0.081133497 
##              Gender.fctrM             Hhold.fctrMKy 
##              -0.070364216              -0.151860222 
##             Hhold.fctrPKn             Hhold.fctrSKn 
##               0.534578849               0.033885621 
##             Hhold.fctrSKy             Income.fctr.Q 
##               0.114752162              -0.071829866 
##             Income.fctr.C             Income.fctr^4 
##              -0.130161899              -0.019455874 
##             Income.fctr^6            Q100010.fctrNo 
##               0.006025901               0.036436326 
##           Q100680.fctrYes           Q100689.fctrYes 
##               0.004776233               0.110778663 
##           Q101163.fctrDad           Q101163.fctrMom 
##              -0.093942855               0.110568655 
##           Q102687.fctrYes           Q103293.fctrYes 
##               0.035917303               0.009187328 
##            Q104996.fctrNo           Q104996.fctrYes 
##              -0.027174935               0.030840441 
##           Q105655.fctrYes            Q106042.fctrNo 
##              -0.047754402              -0.022572851 
##            Q106272.fctrNo           Q106272.fctrYes 
##               0.018750136              -0.033094547 
##            Q106389.fctrNo   Q106997.fctrGrrr people 
##              -0.081395435              -0.028816760 
##   Q106997.fctrYay people!           Q107491.fctrYes 
##               0.081972579               0.029114800 
##            Q107869.fctrNo        Q108342.fctrOnline 
##               0.002204830               0.070638186 
##          Q108855.fctrYes! Q108950.fctrRisk-friendly 
##              -0.060370840               0.045240840 
##            Q109244.fctrNo           Q109244.fctrYes 
##              -0.368068961               0.798909343 
##           Q110740.fctrMac            Q110740.fctrPC 
##               0.022194873              -0.094040223 
##           Q111220.fctrYes           Q111848.fctrYes 
##               0.105711882               0.026423743 
##           Q112270.fctrYes            Q112478.fctrNo 
##               0.011178188              -0.058869716 
##            Q113181.fctrNo           Q113181.fctrYes 
##               0.188992531              -0.200672215 
##           Q113992.fctrYes    Q114386.fctrMysterious 
##               0.018408206               0.008787278 
##           Q115195.fctrYes            Q115390.fctrNo 
##               0.006667928              -0.080608044 
##           Q115390.fctrYes            Q115611.fctrNo 
##               0.020849025               0.133196312 
##           Q115611.fctrYes Q115899.fctrCircumstances 
##              -0.339016722               0.089628166 
##            Q115899.fctrMe          Q116197.fctrA.M. 
##              -0.014103408              -0.033977326 
##         Q116881.fctrHappy         Q116881.fctrRight 
##               0.081808928              -0.134553463 
##            Q116953.fctrNo           Q116953.fctrYes 
##              -0.029073266               0.059821703 
##    Q117186.fctrHot headed      Q118232.fctrIdealist 
##              -0.017114236               0.109817378 
##            Q118233.fctrNo           Q118233.fctrYes 
##              -0.012865284               0.016738520 
##        Q119650.fctrGiving            Q119851.fctrNo 
##              -0.022919294              -0.111428922 
##           Q119851.fctrYes           Q120012.fctrYes 
##               0.019736959               0.041230245 
##            Q120014.fctrNo           Q120014.fctrYes 
##               0.037876336              -0.030635600 
##   Q120194.fctrStudy first            Q120379.fctrNo 
##               0.064690194              -0.045827828 
##           Q120379.fctrYes       Q120472.fctrScience 
##               0.108653701              -0.028048835 
##           Q120650.fctrYes            Q121699.fctrNo 
##              -0.031043954              -0.063199374 
##           Q121699.fctrYes            Q121700.fctrNo 
##               0.055354400              -0.011608644 
##           Q121700.fctrYes           Q122120.fctrYes 
##               0.019772249              -0.039779331 
##            Q122771.fctrPt            Q123464.fctrNo 
##              -0.129074066              -0.018364756 
##            Q124122.fctrNo           Q124122.fctrYes 
##              -0.026727216               0.001702308 
##            Q124742.fctrNo            YOB.Age.fctr.L 
##               0.034842422               0.133354527 
##            YOB.Age.fctr.Q            YOB.Age.fctr^4 
##               0.022697089               0.051333522 
##            YOB.Age.fctr^6            YOB.Age.fctr^7 
##               0.014400447              -0.046938593 
##            YOB.Age.fctr^8 
##              -0.071194408
## [1] TRUE
# From here to save(), this should all be in one function
#   these are executed in the same seq twice more:
#       fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))
## [1] "All.X##rcv#glmnet fit prediction diagnostics:"
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId, 
                                 rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))
## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId, 
                                     rsp_var = glb_rsp_var)

print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))
##                            All.X..rcv.glmnet.imp         imp
## Q109244.fctrYes                      100.0000000 100.0000000
## Hhold.fctrPKn                         66.6656452  66.6656452
## Q109244.fctrNo                        46.0415425  46.0415425
## Q115611.fctrYes                       42.3575968  42.3575968
## Q113181.fctrYes                       25.0555685  25.0555685
## Q113181.fctrNo                        23.6329372  23.6329372
## Hhold.fctrMKy                         18.9960973  18.9960973
## Q116881.fctrRight                     16.7915189  16.7915189
## Q115611.fctrNo                        16.7393728  16.7393728
## YOB.Age.fctr.L                        16.3652951  16.3652951
## Income.fctr.C                         16.1088990  16.1088990
## Q122771.fctrPt                        16.0128680  16.0128680
## Edn.fctr^4                            14.7272891  14.7272891
## Hhold.fctrSKy                         14.1476584  14.1476584
## Q119851.fctrNo                        13.9003500  13.9003500
## Q101163.fctrMom                       13.8663632  13.8663632
## Q100689.fctrYes                       13.7218358  13.7218358
## Q118232.fctrIdealist                  13.6243021  13.6243021
## Q120379.fctrYes                       13.4638696  13.4638696
## Q111220.fctrYes                       13.1107000  13.1107000
## Q101163.fctrDad                       11.6570297  11.6570297
## Q110740.fctrPC                        11.6494388  11.6494388
## Q115899.fctrCircumstances             11.1423796  11.1423796
## Q116881.fctrHappy                     10.1727145  10.1727145
## Q106997.fctrYay people!               10.1564110  10.1564110
## Q106389.fctrNo                        10.0599893  10.0599893
## Edn.fctr^7                            10.0345380  10.0345380
## Q115390.fctrNo                         9.9724910   9.9724910
## Income.fctr.Q                          8.9136429   8.9136429
## Gender.fctrM                           8.8332377   8.8332377
## Q108342.fctrOnline                     8.7546733   8.7546733
## YOB.Age.fctr^8                         8.7475866   8.7475866
## Q120194.fctrStudy first                7.9913298   7.9913298
## Q121699.fctrNo                         7.9896777   7.9896777
## Q108855.fctrYes!                       7.4345962   7.4345962
## Q116953.fctrYes                        7.3519595   7.3519595
## Q112478.fctrNo                         7.2332361   7.2332361
## Q121699.fctrYes                        6.7652199   6.7652199
## YOB.Age.fctr^4                         6.2269952   6.2269952
## Q105655.fctrYes                        5.8365204   5.8365204
## Q120379.fctrNo                         5.7456388   5.7456388
## YOB.Age.fctr^7                         5.6993965   5.6993965
## Q108950.fctrRisk-friendly              5.5519375   5.5519375
## Q120012.fctrYes                        5.0658170   5.0658170
## Q122120.fctrYes                        4.8601449   4.8601449
## Q120014.fctrNo                         4.6517561   4.6517561
## Q100010.fctrNo                         4.4157772   4.4157772
## Q102687.fctrYes                        4.3964689   4.3964689
## Q124742.fctrNo                         4.1882314   4.1882314
## Hhold.fctrSKn                          4.1103602   4.1103602
## Q116197.fctrA.M.                       4.0776867   4.0776867
## Q106272.fctrYes                        4.0430812   4.0430812
## Q120014.fctrYes                        3.7895513   3.7895513
## Q104996.fctrYes                        3.7803145   3.7803145
## Q120650.fctrYes                        3.7720422   3.7720422
## Edn.fctr^6                             3.5996645   3.5996645
## Q116953.fctrNo                         3.5843074   3.5843074
## Q107491.fctrYes                        3.5353385   3.5353385
## Q106997.fctrGrrr people                3.5180627   3.5180627
## Q120472.fctrScience                    3.4813542   3.4813542
## Q104996.fctrNo                         3.3283245   3.3283245
## Q124122.fctrNo                         3.2593588   3.2593588
## Q111848.fctrYes                        3.2294247   3.2294247
## Q106042.fctrNo                         2.7845529   2.7845529
## Q110740.fctrMac                        2.7825530   2.7825530
## Q119650.fctrGiving                     2.7348762   2.7348762
## Q115390.fctrYes                        2.5974711   2.5974711
## YOB.Age.fctr.Q                         2.5213728   2.5213728
## Q121700.fctrYes                        2.4722157   2.4722157
## Q119851.fctrYes                        2.4358735   2.4358735
## Q106272.fctrNo                         2.3239495   2.3239495
## Income.fctr^4                          2.3095349   2.3095349
## Q123464.fctrNo                         2.1880638   2.1880638
## Q113992.fctrYes                        2.1706753   2.1706753
## Q118233.fctrYes                        2.0281910   2.0281910
## Q117186.fctrHot headed                 2.0124127   2.0124127
## Q115899.fctrMe                         1.7457055   1.7457055
## YOB.Age.fctr^6                         1.6235574   1.6235574
## Q118233.fctrNo                         1.5023529   1.5023529
## Q121700.fctrNo                         1.3552284   1.3552284
## Q112270.fctrYes                        1.2435335   1.2435335
## Q103293.fctrYes                        1.0137649   1.0137649
## Q114386.fctrMysterious                 0.9378311   0.9378311
## Q115195.fctrYes                        0.7498744   0.7498744
## Income.fctr^6                          0.6285968   0.6285968
## Q100680.fctrYes                        0.5629082   0.5629082
## Q107869.fctrNo                         0.2233418   0.2233418
## Q124122.fctrYes                        0.1724380   0.1724380
## .rnorm                                 0.0000000   0.0000000
## Edn.fctr.L                             0.0000000   0.0000000
## Edn.fctr.Q                             0.0000000   0.0000000
## Edn.fctr.C                             0.0000000   0.0000000
## Edn.fctr^5                             0.0000000   0.0000000
## Gender.fctrF                           0.0000000   0.0000000
## Hhold.fctrMKn                          0.0000000   0.0000000
## Hhold.fctrPKy                          0.0000000   0.0000000
## Income.fctr.L                          0.0000000   0.0000000
## Income.fctr^5                          0.0000000   0.0000000
## Q100010.fctrYes                        0.0000000   0.0000000
## Q100562.fctrNo                         0.0000000   0.0000000
## Q100562.fctrYes                        0.0000000   0.0000000
## Q100680.fctrNo                         0.0000000   0.0000000
## Q100689.fctrNo                         0.0000000   0.0000000
## Q101162.fctrOptimist                   0.0000000   0.0000000
## Q101162.fctrPessimist                  0.0000000   0.0000000
## Q101596.fctrNo                         0.0000000   0.0000000
## Q101596.fctrYes                        0.0000000   0.0000000
## Q102089.fctrOwn                        0.0000000   0.0000000
## Q102089.fctrRent                       0.0000000   0.0000000
## Q102289.fctrNo                         0.0000000   0.0000000
## Q102289.fctrYes                        0.0000000   0.0000000
## Q102674.fctrNo                         0.0000000   0.0000000
## Q102674.fctrYes                        0.0000000   0.0000000
## Q102687.fctrNo                         0.0000000   0.0000000
## Q102906.fctrNo                         0.0000000   0.0000000
## Q102906.fctrYes                        0.0000000   0.0000000
## Q103293.fctrNo                         0.0000000   0.0000000
## Q105655.fctrNo                         0.0000000   0.0000000
## Q105840.fctrNo                         0.0000000   0.0000000
## Q105840.fctrYes                        0.0000000   0.0000000
## Q106042.fctrYes                        0.0000000   0.0000000
## Q106388.fctrNo                         0.0000000   0.0000000
## Q106388.fctrYes                        0.0000000   0.0000000
## Q106389.fctrYes                        0.0000000   0.0000000
## Q106993.fctrNo                         0.0000000   0.0000000
## Q106993.fctrYes                        0.0000000   0.0000000
## Q107491.fctrNo                         0.0000000   0.0000000
## Q107869.fctrYes                        0.0000000   0.0000000
## Q108342.fctrIn-person                  0.0000000   0.0000000
## Q108343.fctrNo                         0.0000000   0.0000000
## Q108343.fctrYes                        0.0000000   0.0000000
## Q108617.fctrNo                         0.0000000   0.0000000
## Q108617.fctrYes                        0.0000000   0.0000000
## Q108754.fctrNo                         0.0000000   0.0000000
## Q108754.fctrYes                        0.0000000   0.0000000
## Q108855.fctrUmm...                     0.0000000   0.0000000
## Q108856.fctrSocialize                  0.0000000   0.0000000
## Q108856.fctrSpace                      0.0000000   0.0000000
## Q108950.fctrCautious                   0.0000000   0.0000000
## Q109367.fctrNo                         0.0000000   0.0000000
## Q109367.fctrYes                        0.0000000   0.0000000
## Q111220.fctrNo                         0.0000000   0.0000000
## Q111580.fctrDemanding                  0.0000000   0.0000000
## Q111580.fctrSupportive                 0.0000000   0.0000000
## Q111848.fctrNo                         0.0000000   0.0000000
## Q112270.fctrNo                         0.0000000   0.0000000
## Q112478.fctrYes                        0.0000000   0.0000000
## Q112512.fctrNo                         0.0000000   0.0000000
## Q112512.fctrYes                        0.0000000   0.0000000
## Q113583.fctrTalk                       0.0000000   0.0000000
## Q113583.fctrTunes                      0.0000000   0.0000000
## Q113584.fctrPeople                     0.0000000   0.0000000
## Q113584.fctrTechnology                 0.0000000   0.0000000
## Q113992.fctrNo                         0.0000000   0.0000000
## Q114152.fctrNo                         0.0000000   0.0000000
## Q114152.fctrYes                        0.0000000   0.0000000
## Q114386.fctrTMI                        0.0000000   0.0000000
## Q114517.fctrNo                         0.0000000   0.0000000
## Q114517.fctrYes                        0.0000000   0.0000000
## Q114748.fctrNo                         0.0000000   0.0000000
## Q114748.fctrYes                        0.0000000   0.0000000
## Q114961.fctrNo                         0.0000000   0.0000000
## Q114961.fctrYes                        0.0000000   0.0000000
## Q115195.fctrNo                         0.0000000   0.0000000
## Q115602.fctrNo                         0.0000000   0.0000000
## Q115602.fctrYes                        0.0000000   0.0000000
## Q115610.fctrNo                         0.0000000   0.0000000
## Q115610.fctrYes                        0.0000000   0.0000000
## Q115777.fctrEnd                        0.0000000   0.0000000
## Q115777.fctrStart                      0.0000000   0.0000000
## Q116197.fctrP.M.                       0.0000000   0.0000000
## Q116441.fctrNo                         0.0000000   0.0000000
## Q116441.fctrYes                        0.0000000   0.0000000
## Q116448.fctrNo                         0.0000000   0.0000000
## Q116448.fctrYes                        0.0000000   0.0000000
## Q116601.fctrNo                         0.0000000   0.0000000
## Q116601.fctrYes                        0.0000000   0.0000000
## Q116797.fctrNo                         0.0000000   0.0000000
## Q116797.fctrYes                        0.0000000   0.0000000
## Q117186.fctrCool headed                0.0000000   0.0000000
## Q117193.fctrOdd hours                  0.0000000   0.0000000
## Q117193.fctrStandard hours             0.0000000   0.0000000
## Q118117.fctrNo                         0.0000000   0.0000000
## Q118117.fctrYes                        0.0000000   0.0000000
## Q118232.fctrPragmatist                 0.0000000   0.0000000
## Q118237.fctrNo                         0.0000000   0.0000000
## Q118237.fctrYes                        0.0000000   0.0000000
## Q118892.fctrNo                         0.0000000   0.0000000
## Q118892.fctrYes                        0.0000000   0.0000000
## Q119334.fctrNo                         0.0000000   0.0000000
## Q119334.fctrYes                        0.0000000   0.0000000
## Q119650.fctrReceiving                  0.0000000   0.0000000
## Q120012.fctrNo                         0.0000000   0.0000000
## Q120194.fctrTry first                  0.0000000   0.0000000
## Q120472.fctrArt                        0.0000000   0.0000000
## Q120650.fctrNo                         0.0000000   0.0000000
## Q120978.fctrNo                         0.0000000   0.0000000
## Q120978.fctrYes                        0.0000000   0.0000000
## Q121011.fctrNo                         0.0000000   0.0000000
## Q121011.fctrYes                        0.0000000   0.0000000
## Q122120.fctrNo                         0.0000000   0.0000000
## Q122769.fctrNo                         0.0000000   0.0000000
## Q122769.fctrYes                        0.0000000   0.0000000
## Q122770.fctrNo                         0.0000000   0.0000000
## Q122770.fctrYes                        0.0000000   0.0000000
## Q122771.fctrPc                         0.0000000   0.0000000
## Q123464.fctrYes                        0.0000000   0.0000000
## Q123621.fctrNo                         0.0000000   0.0000000
## Q123621.fctrYes                        0.0000000   0.0000000
## Q124742.fctrYes                        0.0000000   0.0000000
## YOB.Age.fctr.C                         0.0000000   0.0000000
## YOB.Age.fctr^5                         0.0000000   0.0000000
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))

# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
    if (!is.null(featsimp_df <- glb_featsimp_df)) {
        featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))    
        featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
        featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)    
        featsimp_df$feat.interact <- 
            ifelse(featsimp_df$feat.interact == featsimp_df$feat, 
                                            NA, featsimp_df$feat.interact)
        featsimp_df$feat <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
        featsimp_df$feat.interact <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact) 
        featsimp_df <- orderBy(~ -imp.max, 
            summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
                      FUN=max))    
        #rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])    
        
        featsimp_df <- subset(featsimp_df, !is.na(imp.max))
        if (nrow(featsimp_df) > 5) {
            warning("Limiting important feature scatter plots to 5 out of ",
                    nrow(featsimp_df))
            featsimp_df <- head(featsimp_df, 5)
        }
        
    #     if (!all(is.na(featsimp_df$feat.interact)))
    #         stop("not implemented yet")
        rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
        for (var in featsimp_df$feat) {
            plot_df <- melt(obs_df, id.vars = var, 
                            measure.vars = c(glb_rsp_var, rsp_var_out))
    
            print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
                                facet_colcol_name = "variable", jitter = TRUE) + 
                          guides(color = FALSE))
        }
    }
    
    if (glb_is_regression) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No important features in glb_fin_mdl") else
            print(myplot_prediction_regression(df=obs_df, 
                        feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
                                      ".rownames"), 
                                               feat_y=featsimp_df$feat[1],
                        rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
                        id_vars=glbFeatsId)
    #               + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
    #               + geom_point(aes_string(color="<col_name>.fctr")) #  to color the plot
                  )
    }    
    
    if (glb_is_classification) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No features in selected model are statistically important")
        else print(myplot_prediction_classification(df = obs_df, 
                                feat_x = ifelse(nrow(featsimp_df) > 1, 
                                                featsimp_df$feat[2], ".rownames"),
                                               feat_y = featsimp_df$feat[1],
                                                rsp_var = glb_rsp_var, 
                                                rsp_var_out = rsp_var_out, 
                                                id_vars = glbFeatsId,
                                                prob_threshold = prob_threshold))
    }    
}

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId, 
            prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId, 
                                           "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 97

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    1187          D                         0.2294075
## 2    2798          D                         0.2478459
## 3    1393          D                         0.2620899
## 4     943          D                         0.2629215
## 5    1843          D                         0.2678920
## 6    1045          D                         0.2691518
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            R                             TRUE
## 2                            R                             TRUE
## 3                            R                             TRUE
## 4                            R                             TRUE
## 5                            R                             TRUE
## 6                            R                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.7705925                               FALSE
## 2                            0.7521541                               FALSE
## 3                            0.7379101                               FALSE
## 4                            0.7370785                               FALSE
## 5                            0.7321080                               FALSE
## 6                            0.7308482                               FALSE
##   Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                         -0.4705925
## 2                                 FALSE                         -0.4521541
## 3                                 FALSE                         -0.4379101
## 4                                 FALSE                         -0.4370785
## 5                                 FALSE                         -0.4321080
## 6                                 FALSE                         -0.4308482
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 12      697          D                         0.2982021
## 105    1261          D                         0.4524387
## 205    6755          D                         0.5220793
## 250    1411          D                         0.5416549
## 306     901          D                         0.5632237
## 448    3821          D                         0.6716883
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 12                             R                             TRUE
## 105                            R                             TRUE
## 205                            R                             TRUE
## 250                            R                             TRUE
## 306                            R                             TRUE
## 448                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 12                             0.7017979
## 105                            0.5475613
## 205                            0.4779207
## 250                            0.4583451
## 306                            0.4367763
## 448                            0.3283117
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 12                                FALSE
## 105                               FALSE
## 205                               FALSE
## 250                               FALSE
## 306                               FALSE
## 448                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 12                                  FALSE
## 105                                 FALSE
## 205                                 FALSE
## 250                                 FALSE
## 306                                 FALSE
## 448                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 12                         -0.40179786
## 105                        -0.24756127
## 205                        -0.17792067
## 250                        -0.15834505
## 306                        -0.13677628
## 448                        -0.02831165
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 493     520          R                         0.8285645
## 494    5466          R                         0.8350278
## 495    2957          R                         0.8384858
## 496    5148          R                         0.8428523
## 497    1307          R                         0.8473741
## 498     451          R                         0.8712485
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 493                            D                             TRUE
## 494                            D                             TRUE
## 495                            D                             TRUE
## 496                            D                             TRUE
## 497                            D                             TRUE
## 498                            D                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 493                            0.8285645
## 494                            0.8350278
## 495                            0.8384858
## 496                            0.8428523
## 497                            0.8473741
## 498                            0.8712485
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 493                               FALSE
## 494                               FALSE
## 495                               FALSE
## 496                               FALSE
## 497                               FALSE
## 498                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 493                                 FALSE
## 494                                 FALSE
## 495                                 FALSE
## 496                                 FALSE
## 497                                 FALSE
## 498                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 493                          0.1285645
## 494                          0.1350278
## 495                          0.1384858
## 496                          0.1428523
## 497                          0.1473741
## 498                          0.1712485

if (!is.null(glbFeatsCategory)) {
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId, 
                                 label = "fit"), 
                            by = glbFeatsCategory, all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
                                 label="OOB"),
                          #by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
                          all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    if (any(grepl("OOB", glbMdlMetricsEval)))
        print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
            print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
    print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}
##     Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy        PKy      9     52     10     0.01169065    0.008035714
## PKn        PKn     30    150     37     0.03372302    0.026785714
## N            N     83    367    102     0.08250899    0.074107143
## SKn        SKn    511   1920    638     0.43165468    0.456250000
## MKn        MKn    136    516    169     0.11600719    0.121428571
## SKy        SKy     53    147     65     0.03304856    0.047321429
## MKy        MKy    298   1296    371     0.29136691    0.266071429
##     .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy    0.007183908        24.52672        0.4716676     52        4.439736
## PKn    0.026580460        54.07667        0.3605111    150       14.275241
## N      0.073275862       170.90149        0.4656716    367       37.875827
## SKn    0.458333333       861.17518        0.4485287   1920      232.827797
## MKn    0.121408046       230.56787        0.4468370    516       61.957461
## SKy    0.046695402        62.64058        0.4261264    147       23.920386
## MKy    0.266522989       575.87008        0.4443442   1296      132.600733
##     err.abs.OOB.mean
## PKy        0.4933040
## PKn        0.4758414
## N          0.4563353
## SKn        0.4556317
## MKn        0.4555696
## SKy        0.4513280
## MKy        0.4449689
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##      1120.000000      4448.000000      1392.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000      1979.758590         3.063687 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##      4448.000000       507.897181         3.232979
write.csv(glbObsOOB[, c(glbFeatsId, 
                grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))], 
    paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE), 
           "_OOBobs.csv"), row.names=FALSE)

fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 434.365  NA      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 18 fit.models          8          2           2 422.512 434.376  11.864
## 19 fit.models          8          3           3 434.376      NA      NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 19        fit.models          8          3           3 434.376 438.901
## 20 fit.data.training          9          0           0 438.901      NA
##    elapsed
## 19   4.525
## 20      NA

Step 9.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else 
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{    
    warning("Final model same as glbMdlSelId")
    glbMdlFinId <- paste0("Final.", glbMdlSelId)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
    mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
    mdlDf$id <- glbMdlFinId
    glb_models_df <- rbind(glb_models_df, mdlDf)
} else {    
            if (grepl("RFE\\.X", names(glbMdlFamilies))) {
                indepVar <- mygetIndepVar(glb_feats_df)
                rfe_trn_results <- 
                    myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
                if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
                                      sort(predictors(rfe_fit_results))))) {
                    print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
                    print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
                    print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
                    print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
            }
        }
    # }    

    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        # Fit selected models on glbObsTrn
        for (mdl_id in gsub(".prob", "", 
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
                            fixed = TRUE)) {
            mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
            mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"), 
                               collapse = ".")
            if (grepl("RFE\\.X\\.", mdlIdPfx)) 
                mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
                    predictors(rfe_trn_results))) else
                mdlIndepVars <- trim(unlist(
            strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            ret_lst <- 
                myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = mdlIdPfx, 
                        type = glb_model_type, tune.df = glbMdlTuneParams,
                        trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = tail(mdl_id_components, 1))),
                    indepVar = mdlIndepVars,
                    rsp_var = glb_rsp_var, 
                    fit_df = glbObsTrn, OOB_df = NULL)
            
            glbObsTrn <- glb_get_predictions(df = glbObsTrn,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glbObsNew <- glb_get_predictions(df = glbObsNew,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
        
    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        if (glb_is_classification && glb_is_binomial)
            indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
                                    row.names(mdlimp_df)) else
            indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
                                    row.names(mdlimp_df))
    } else 
    if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
        indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
    } else indepVar <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glbMdlSelId
                                                   , "feats"], "[,]")))
        
    if (!is.null(glb_preproc_methods) &&
        ((match_pos <- regexpr(gsub(".", "\\.", 
                                    paste(glb_preproc_methods, collapse = "|"),
                                   fixed = TRUE), glbMdlSelId)) != -1))
        ths_preProcess <- str_sub(glbMdlSelId, match_pos, 
                                match_pos + attr(match_pos, "match.length") - 1) else
        ths_preProcess <- NULL                                      

    mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
                                   "Final.Ensemble", "Final")
    
    trnobs_df <- glbObsTrn 
    if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
        trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
        print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
    }    
        
    # Force fitting of Final.glm to identify outliers
    method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
    for (method in method_vctr) {
        #source("caret_nominalTrainWorkflow.R")
        
        # glmnet requires at least 2 indep vars
        if ((length(indepVar) == 1) && (method %in% "glmnet"))
            next
        
        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = mdl_id_pfx, 
                    type = glb_model_type, trainControl.method = "repeatedcv",
                    trainControl.number = glb_rcv_n_folds, 
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    trainControl.allowParallel = glbMdlAllowParallel,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method,
                    train.preProcess = ths_preProcess)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = trnobs_df, OOB_df = NULL)
        
        if ((length(method_vctr) == 1) || (method != "glm")) {
            glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]] 
            glbMdlFinId <- glb_models_df[length(glb_models_lst), "id"]
        }
    }
        
}
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.697000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0113 on full training set
## [1] "myfit_mdl: train complete: 23.170000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha

##             Length Class      Mode     
## a0             78  -none-     numeric  
## beta        16536  dgCMatrix  S4       
## df             78  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         78  -none-     numeric  
## dev.ratio      78  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        212  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)                Edn.fctr.L 
##               0.199317507               0.010575818 
##              Gender.fctrM             Hhold.fctrMKy 
##              -0.089353036              -0.053644126 
##             Hhold.fctrPKn             Income.fctr.Q 
##               0.351733652              -0.027041500 
##             Income.fctr.C           Q100689.fctrYes 
##              -0.019452829               0.064278964 
##           Q101163.fctrDad           Q101163.fctrMom 
##              -0.030502858               0.100451334 
##   Q106997.fctrGrrr people          Q108855.fctrYes! 
##              -0.011683852              -0.010941409 
##            Q109244.fctrNo           Q109244.fctrYes 
##              -0.329607513               0.973614651 
##            Q110740.fctrPC            Q113181.fctrNo 
##              -0.059059061               0.190175476 
##           Q113181.fctrYes           Q115390.fctrYes 
##              -0.245302116               0.028506902 
##            Q115611.fctrNo           Q115611.fctrYes 
##               0.151059581              -0.317423919 
## Q115899.fctrCircumstances         Q116881.fctrRight 
##               0.028498235              -0.147331307 
##      Q118232.fctrIdealist            Q119851.fctrNo 
##               0.061143167              -0.074663390 
##   Q120194.fctrStudy first           Q120379.fctrYes 
##               0.003760908               0.049220750 
##       Q120472.fctrScience           Q121699.fctrYes 
##              -0.038845973               0.020085224 
##            Q122771.fctrPt            YOB.Age.fctr.L 
##              -0.045014766               0.007388581 
## [1] "max lambda < lambdaOpt:"
##               (Intercept)                Edn.fctr.L 
##               0.205373851               0.017722336 
##              Gender.fctrM             Hhold.fctrMKy 
##              -0.091935724              -0.068703734 
##             Hhold.fctrPKn             Income.fctr.Q 
##               0.368529532              -0.036198758 
##             Income.fctr.C           Q100689.fctrYes 
##              -0.035966293               0.074786011 
##           Q101163.fctrDad           Q101163.fctrMom 
##              -0.037242891               0.105261638 
##            Q106042.fctrNo            Q106389.fctrNo 
##              -0.002517763              -0.007645282 
##   Q106997.fctrGrrr people          Q108855.fctrYes! 
##              -0.024295419              -0.021977986 
##            Q109244.fctrNo           Q109244.fctrYes 
##              -0.331767116               0.978304078 
##           Q110740.fctrMac            Q110740.fctrPC 
##               0.006567562              -0.063438817 
##            Q112478.fctrNo            Q113181.fctrNo 
##              -0.004436373               0.199435328 
##           Q113181.fctrYes           Q115195.fctrYes 
##              -0.247560048               0.005146233 
##           Q115390.fctrYes            Q115611.fctrNo 
##               0.039119147               0.150595306 
##           Q115611.fctrYes Q115899.fctrCircumstances 
##              -0.327359553               0.038927387 
##         Q116881.fctrRight      Q118232.fctrIdealist 
##              -0.158233644               0.072613253 
##            Q119851.fctrNo   Q120194.fctrStudy first 
##              -0.085729511               0.016460971 
##            Q120379.fctrNo           Q120379.fctrYes 
##              -0.002200789               0.059326120 
##       Q120472.fctrScience           Q121699.fctrYes 
##              -0.049438382               0.029527323 
##           Q122120.fctrYes            Q122771.fctrPt 
##              -0.005265105              -0.060124779 
##            YOB.Age.fctr.L            YOB.Age.fctr^8 
##               0.025718500              -0.005439800 
## [1] "myfit_mdl: train diagnostics complete: 23.809000 secs"

##          Prediction
## Reference    R    D
##         R 2369  248
##         D 2019  932
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.928520e-01   2.129065e-01   5.798116e-01   6.057950e-01   5.299928e-01 
## AccuracyPValue  McnemarPValue 
##   2.244697e-21  1.749427e-302 
## [1] "myfit_mdl: predict complete: 29.242000 secs"
##                  id
## 1 Final##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q101162.fctr,Q115777.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     22.365                  1.96
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6301535    0.5513947    0.7089122       0.3043179
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6        0.676374        0.6290113
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5798116              0.605795     0.2511044
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01287164      0.02604359
## [1] "myfit_mdl: exit: 29.258000 secs"
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)
##                label step_major step_minor label_minor     bgn     end
## 20 fit.data.training          9          0           0 438.901 468.738
## 21 fit.data.training          9          1           1 468.738      NA
##    elapsed
## 20  29.837
## 21      NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glbMdlFinId)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glbMdlFinId)$feats, ","))
    if (glb_is_classification && glb_is_binomial)
        mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    mdlEnsembleComps <- gsub(paste0("^", 
                        gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
                             "", mdlEnsembleComps)
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)
## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.7
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
##                            All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Q109244.fctrYes                      100.0000000           100.0000000
## Hhold.fctrPKn                         66.6656452            36.9944053
## Q109244.fctrNo                        46.0415425            33.8868736
## Q115611.fctrYes                       42.3575968            33.0857210
## Q113181.fctrYes                       25.0555685            25.2568478
## Q113181.fctrNo                        23.6329372            20.0124144
## Q116881.fctrRight                     16.7915189            15.7181326
## Q115611.fctrNo                        16.7393728            15.4468453
## Q101163.fctrMom                       13.8663632            10.5659833
## Gender.fctrM                           8.8332377             9.3011377
## Q119851.fctrNo                        13.9003500             8.2839330
## Q100689.fctrYes                       13.7218358             7.1880951
## Q118232.fctrIdealist                  13.6243021             6.9222265
## Hhold.fctrMKy                         18.9960973             6.3603486
## Q110740.fctrPC                        11.6494388             6.3012963
## Q120379.fctrYes                       13.4638696             5.6225507
## Q122771.fctrPt                        16.0128680             5.4793117
## Q120472.fctrScience                    3.4813542             4.5878163
## Q115390.fctrYes                        2.5974711             3.5298914
## Q115899.fctrCircumstances             11.1423796             3.5184821
## Q101163.fctrDad                       11.6570297             3.5118256
## Income.fctr.Q                          8.9136429             3.2961742
## Income.fctr.C                         16.1088990             2.9415690
## Q121699.fctrYes                        6.7652199             2.5999887
## Q106997.fctrGrrr people                3.5180627             1.9215434
## YOB.Age.fctr.L                        16.3652951             1.8101721
## Q108855.fctrYes!                       7.4345962             1.7549852
## Edn.fctr.L                             0.0000000             1.4939928
## Q120194.fctrStudy first                7.9913298             1.1150559
## Q106389.fctrNo                        10.0599893             0.4393390
## Q110740.fctrMac                        2.7825530             0.3774074
## YOB.Age.fctr^8                         8.7475866             0.3126001
## Q122120.fctrYes                        4.8601449             0.3025612
## Q115195.fctrYes                        0.7498744             0.2957302
## Q112478.fctrNo                         7.2332361             0.2549378
## Q106042.fctrNo                         2.7845529             0.1446842
## Q120379.fctrNo                         5.7456388             0.1264692
## .rnorm                                 0.0000000             0.0000000
## Edn.fctr.C                             0.0000000             0.0000000
## Edn.fctr.Q                             0.0000000             0.0000000
## Edn.fctr^4                            14.7272891             0.0000000
## Edn.fctr^5                             0.0000000             0.0000000
## Edn.fctr^6                             3.5996645             0.0000000
## Edn.fctr^7                            10.0345380             0.0000000
## Gender.fctrF                           0.0000000             0.0000000
## Hhold.fctrMKn                          0.0000000             0.0000000
## Hhold.fctrPKy                          0.0000000             0.0000000
## Hhold.fctrSKn                          4.1103602             0.0000000
## Hhold.fctrSKy                         14.1476584             0.0000000
## Income.fctr.L                          0.0000000             0.0000000
## Income.fctr^4                          2.3095349             0.0000000
## Income.fctr^5                          0.0000000             0.0000000
## Income.fctr^6                          0.6285968             0.0000000
## Q100010.fctrNo                         4.4157772             0.0000000
## Q100010.fctrYes                        0.0000000             0.0000000
## Q100562.fctrNo                         0.0000000             0.0000000
## Q100562.fctrYes                        0.0000000             0.0000000
## Q100680.fctrNo                         0.0000000             0.0000000
## Q100680.fctrYes                        0.5629082             0.0000000
## Q100689.fctrNo                         0.0000000             0.0000000
## Q101162.fctrOptimist                   0.0000000             0.0000000
## Q101162.fctrPessimist                  0.0000000             0.0000000
## Q101596.fctrNo                         0.0000000             0.0000000
## Q101596.fctrYes                        0.0000000             0.0000000
## Q102089.fctrOwn                        0.0000000             0.0000000
## Q102089.fctrRent                       0.0000000             0.0000000
## Q102289.fctrNo                         0.0000000             0.0000000
## Q102289.fctrYes                        0.0000000             0.0000000
## Q102674.fctrNo                         0.0000000             0.0000000
## Q102674.fctrYes                        0.0000000             0.0000000
## Q102687.fctrNo                         0.0000000             0.0000000
## Q102687.fctrYes                        4.3964689             0.0000000
## Q102906.fctrNo                         0.0000000             0.0000000
## Q102906.fctrYes                        0.0000000             0.0000000
## Q103293.fctrNo                         0.0000000             0.0000000
## Q103293.fctrYes                        1.0137649             0.0000000
## Q104996.fctrNo                         3.3283245             0.0000000
## Q104996.fctrYes                        3.7803145             0.0000000
## Q105655.fctrNo                         0.0000000             0.0000000
## Q105655.fctrYes                        5.8365204             0.0000000
## Q105840.fctrNo                         0.0000000             0.0000000
## Q105840.fctrYes                        0.0000000             0.0000000
## Q106042.fctrYes                        0.0000000             0.0000000
## Q106272.fctrNo                         2.3239495             0.0000000
## Q106272.fctrYes                        4.0430812             0.0000000
## Q106388.fctrNo                         0.0000000             0.0000000
## Q106388.fctrYes                        0.0000000             0.0000000
## Q106389.fctrYes                        0.0000000             0.0000000
## Q106993.fctrNo                         0.0000000             0.0000000
## Q106993.fctrYes                        0.0000000             0.0000000
## Q106997.fctrYay people!               10.1564110             0.0000000
## Q107491.fctrNo                         0.0000000             0.0000000
## Q107491.fctrYes                        3.5353385             0.0000000
## Q107869.fctrNo                         0.2233418             0.0000000
## Q107869.fctrYes                        0.0000000             0.0000000
## Q108342.fctrIn-person                  0.0000000             0.0000000
## Q108342.fctrOnline                     8.7546733             0.0000000
## Q108343.fctrNo                         0.0000000             0.0000000
## Q108343.fctrYes                        0.0000000             0.0000000
## Q108617.fctrNo                         0.0000000             0.0000000
## Q108617.fctrYes                        0.0000000             0.0000000
## Q108754.fctrNo                         0.0000000             0.0000000
## Q108754.fctrYes                        0.0000000             0.0000000
## Q108855.fctrUmm...                     0.0000000             0.0000000
## Q108856.fctrSocialize                  0.0000000             0.0000000
## Q108856.fctrSpace                      0.0000000             0.0000000
## Q108950.fctrCautious                   0.0000000             0.0000000
## Q108950.fctrRisk-friendly              5.5519375             0.0000000
## Q109367.fctrNo                         0.0000000             0.0000000
## Q109367.fctrYes                        0.0000000             0.0000000
## Q111220.fctrNo                         0.0000000             0.0000000
## Q111220.fctrYes                       13.1107000             0.0000000
## Q111580.fctrDemanding                  0.0000000             0.0000000
## Q111580.fctrSupportive                 0.0000000             0.0000000
## Q111848.fctrNo                         0.0000000             0.0000000
## Q111848.fctrYes                        3.2294247             0.0000000
## Q112270.fctrNo                         0.0000000             0.0000000
## Q112270.fctrYes                        1.2435335             0.0000000
## Q112478.fctrYes                        0.0000000             0.0000000
## Q112512.fctrNo                         0.0000000             0.0000000
## Q112512.fctrYes                        0.0000000             0.0000000
## Q113583.fctrTalk                       0.0000000             0.0000000
## Q113583.fctrTunes                      0.0000000             0.0000000
## Q113584.fctrPeople                     0.0000000             0.0000000
## Q113584.fctrTechnology                 0.0000000             0.0000000
## Q113992.fctrNo                         0.0000000             0.0000000
## Q113992.fctrYes                        2.1706753             0.0000000
## Q114152.fctrNo                         0.0000000             0.0000000
## Q114152.fctrYes                        0.0000000             0.0000000
## Q114386.fctrMysterious                 0.9378311             0.0000000
## Q114386.fctrTMI                        0.0000000             0.0000000
## Q114517.fctrNo                         0.0000000             0.0000000
## Q114517.fctrYes                        0.0000000             0.0000000
## Q114748.fctrNo                         0.0000000             0.0000000
## Q114748.fctrYes                        0.0000000             0.0000000
## Q114961.fctrNo                         0.0000000             0.0000000
## Q114961.fctrYes                        0.0000000             0.0000000
## Q115195.fctrNo                         0.0000000             0.0000000
## Q115390.fctrNo                         9.9724910             0.0000000
## Q115602.fctrNo                         0.0000000             0.0000000
## Q115602.fctrYes                        0.0000000             0.0000000
## Q115610.fctrNo                         0.0000000             0.0000000
## Q115610.fctrYes                        0.0000000             0.0000000
## Q115777.fctrEnd                        0.0000000             0.0000000
## Q115777.fctrStart                      0.0000000             0.0000000
## Q115899.fctrMe                         1.7457055             0.0000000
## Q116197.fctrA.M.                       4.0776867             0.0000000
## Q116197.fctrP.M.                       0.0000000             0.0000000
## Q116441.fctrNo                         0.0000000             0.0000000
## Q116441.fctrYes                        0.0000000             0.0000000
## Q116448.fctrNo                         0.0000000             0.0000000
## Q116448.fctrYes                        0.0000000             0.0000000
## Q116601.fctrNo                         0.0000000             0.0000000
## Q116601.fctrYes                        0.0000000             0.0000000
## Q116797.fctrNo                         0.0000000             0.0000000
## Q116797.fctrYes                        0.0000000             0.0000000
## Q116881.fctrHappy                     10.1727145             0.0000000
## Q116953.fctrNo                         3.5843074             0.0000000
## Q116953.fctrYes                        7.3519595             0.0000000
## Q117186.fctrCool headed                0.0000000             0.0000000
## Q117186.fctrHot headed                 2.0124127             0.0000000
## Q117193.fctrOdd hours                  0.0000000             0.0000000
## Q117193.fctrStandard hours             0.0000000             0.0000000
## Q118117.fctrNo                         0.0000000             0.0000000
## Q118117.fctrYes                        0.0000000             0.0000000
## Q118232.fctrPragmatist                 0.0000000             0.0000000
## Q118233.fctrNo                         1.5023529             0.0000000
## Q118233.fctrYes                        2.0281910             0.0000000
## Q118237.fctrNo                         0.0000000             0.0000000
## Q118237.fctrYes                        0.0000000             0.0000000
## Q118892.fctrNo                         0.0000000             0.0000000
## Q118892.fctrYes                        0.0000000             0.0000000
## Q119334.fctrNo                         0.0000000             0.0000000
## Q119334.fctrYes                        0.0000000             0.0000000
## Q119650.fctrGiving                     2.7348762             0.0000000
## Q119650.fctrReceiving                  0.0000000             0.0000000
## Q119851.fctrYes                        2.4358735             0.0000000
## Q120012.fctrNo                         0.0000000             0.0000000
## Q120012.fctrYes                        5.0658170             0.0000000
## Q120014.fctrNo                         4.6517561             0.0000000
## Q120014.fctrYes                        3.7895513             0.0000000
## Q120194.fctrTry first                  0.0000000             0.0000000
## Q120472.fctrArt                        0.0000000             0.0000000
## Q120650.fctrNo                         0.0000000             0.0000000
## Q120650.fctrYes                        3.7720422             0.0000000
## Q120978.fctrNo                         0.0000000             0.0000000
## Q120978.fctrYes                        0.0000000             0.0000000
## Q121011.fctrNo                         0.0000000             0.0000000
## Q121011.fctrYes                        0.0000000             0.0000000
## Q121699.fctrNo                         7.9896777             0.0000000
## Q121700.fctrNo                         1.3552284             0.0000000
## Q121700.fctrYes                        2.4722157             0.0000000
## Q122120.fctrNo                         0.0000000             0.0000000
## Q122769.fctrNo                         0.0000000             0.0000000
## Q122769.fctrYes                        0.0000000             0.0000000
## Q122770.fctrNo                         0.0000000             0.0000000
## Q122770.fctrYes                        0.0000000             0.0000000
## Q122771.fctrPc                         0.0000000             0.0000000
## Q123464.fctrNo                         2.1880638             0.0000000
## Q123464.fctrYes                        0.0000000             0.0000000
## Q123621.fctrNo                         0.0000000             0.0000000
## Q123621.fctrYes                        0.0000000             0.0000000
## Q124122.fctrNo                         3.2593588             0.0000000
## Q124122.fctrYes                        0.1724380             0.0000000
## Q124742.fctrNo                         4.1882314             0.0000000
## Q124742.fctrYes                        0.0000000             0.0000000
## YOB.Age.fctr.C                         0.0000000             0.0000000
## YOB.Age.fctr.Q                         2.5213728             0.0000000
## YOB.Age.fctr^4                         6.2269952             0.0000000
## YOB.Age.fctr^5                         0.0000000             0.0000000
## YOB.Age.fctr^6                         1.6235574             0.0000000
## YOB.Age.fctr^7                         5.6993965             0.0000000
##                                    imp
## Q109244.fctrYes            100.0000000
## Hhold.fctrPKn               36.9944053
## Q109244.fctrNo              33.8868736
## Q115611.fctrYes             33.0857210
## Q113181.fctrYes             25.2568478
## Q113181.fctrNo              20.0124144
## Q116881.fctrRight           15.7181326
## Q115611.fctrNo              15.4468453
## Q101163.fctrMom             10.5659833
## Gender.fctrM                 9.3011377
## Q119851.fctrNo               8.2839330
## Q100689.fctrYes              7.1880951
## Q118232.fctrIdealist         6.9222265
## Hhold.fctrMKy                6.3603486
## Q110740.fctrPC               6.3012963
## Q120379.fctrYes              5.6225507
## Q122771.fctrPt               5.4793117
## Q120472.fctrScience          4.5878163
## Q115390.fctrYes              3.5298914
## Q115899.fctrCircumstances    3.5184821
## Q101163.fctrDad              3.5118256
## Income.fctr.Q                3.2961742
## Income.fctr.C                2.9415690
## Q121699.fctrYes              2.5999887
## Q106997.fctrGrrr people      1.9215434
## YOB.Age.fctr.L               1.8101721
## Q108855.fctrYes!             1.7549852
## Edn.fctr.L                   1.4939928
## Q120194.fctrStudy first      1.1150559
## Q106389.fctrNo               0.4393390
## Q110740.fctrMac              0.3774074
## YOB.Age.fctr^8               0.3126001
## Q122120.fctrYes              0.3025612
## Q115195.fctrYes              0.2957302
## Q112478.fctrNo               0.2549378
## Q106042.fctrNo               0.1446842
## Q120379.fctrNo               0.1264692
## .rnorm                       0.0000000
## Edn.fctr.C                   0.0000000
## Edn.fctr.Q                   0.0000000
## Edn.fctr^4                   0.0000000
## Edn.fctr^5                   0.0000000
## Edn.fctr^6                   0.0000000
## Edn.fctr^7                   0.0000000
## Gender.fctrF                 0.0000000
## Hhold.fctrMKn                0.0000000
## Hhold.fctrPKy                0.0000000
## Hhold.fctrSKn                0.0000000
## Hhold.fctrSKy                0.0000000
## Income.fctr.L                0.0000000
## Income.fctr^4                0.0000000
## Income.fctr^5                0.0000000
## Income.fctr^6                0.0000000
## Q100010.fctrNo               0.0000000
## Q100010.fctrYes              0.0000000
## Q100562.fctrNo               0.0000000
## Q100562.fctrYes              0.0000000
## Q100680.fctrNo               0.0000000
## Q100680.fctrYes              0.0000000
## Q100689.fctrNo               0.0000000
## Q101162.fctrOptimist         0.0000000
## Q101162.fctrPessimist        0.0000000
## Q101596.fctrNo               0.0000000
## Q101596.fctrYes              0.0000000
## Q102089.fctrOwn              0.0000000
## Q102089.fctrRent             0.0000000
## Q102289.fctrNo               0.0000000
## Q102289.fctrYes              0.0000000
## Q102674.fctrNo               0.0000000
## Q102674.fctrYes              0.0000000
## Q102687.fctrNo               0.0000000
## Q102687.fctrYes              0.0000000
## Q102906.fctrNo               0.0000000
## Q102906.fctrYes              0.0000000
## Q103293.fctrNo               0.0000000
## Q103293.fctrYes              0.0000000
## Q104996.fctrNo               0.0000000
## Q104996.fctrYes              0.0000000
## Q105655.fctrNo               0.0000000
## Q105655.fctrYes              0.0000000
## Q105840.fctrNo               0.0000000
## Q105840.fctrYes              0.0000000
## Q106042.fctrYes              0.0000000
## Q106272.fctrNo               0.0000000
## Q106272.fctrYes              0.0000000
## Q106388.fctrNo               0.0000000
## Q106388.fctrYes              0.0000000
## Q106389.fctrYes              0.0000000
## Q106993.fctrNo               0.0000000
## Q106993.fctrYes              0.0000000
## Q106997.fctrYay people!      0.0000000
## Q107491.fctrNo               0.0000000
## Q107491.fctrYes              0.0000000
## Q107869.fctrNo               0.0000000
## Q107869.fctrYes              0.0000000
## Q108342.fctrIn-person        0.0000000
## Q108342.fctrOnline           0.0000000
## Q108343.fctrNo               0.0000000
## Q108343.fctrYes              0.0000000
## Q108617.fctrNo               0.0000000
## Q108617.fctrYes              0.0000000
## Q108754.fctrNo               0.0000000
## Q108754.fctrYes              0.0000000
## Q108855.fctrUmm...           0.0000000
## Q108856.fctrSocialize        0.0000000
## Q108856.fctrSpace            0.0000000
## Q108950.fctrCautious         0.0000000
## Q108950.fctrRisk-friendly    0.0000000
## Q109367.fctrNo               0.0000000
## Q109367.fctrYes              0.0000000
## Q111220.fctrNo               0.0000000
## Q111220.fctrYes              0.0000000
## Q111580.fctrDemanding        0.0000000
## Q111580.fctrSupportive       0.0000000
## Q111848.fctrNo               0.0000000
## Q111848.fctrYes              0.0000000
## Q112270.fctrNo               0.0000000
## Q112270.fctrYes              0.0000000
## Q112478.fctrYes              0.0000000
## Q112512.fctrNo               0.0000000
## Q112512.fctrYes              0.0000000
## Q113583.fctrTalk             0.0000000
## Q113583.fctrTunes            0.0000000
## Q113584.fctrPeople           0.0000000
## Q113584.fctrTechnology       0.0000000
## Q113992.fctrNo               0.0000000
## Q113992.fctrYes              0.0000000
## Q114152.fctrNo               0.0000000
## Q114152.fctrYes              0.0000000
## Q114386.fctrMysterious       0.0000000
## Q114386.fctrTMI              0.0000000
## Q114517.fctrNo               0.0000000
## Q114517.fctrYes              0.0000000
## Q114748.fctrNo               0.0000000
## Q114748.fctrYes              0.0000000
## Q114961.fctrNo               0.0000000
## Q114961.fctrYes              0.0000000
## Q115195.fctrNo               0.0000000
## Q115390.fctrNo               0.0000000
## Q115602.fctrNo               0.0000000
## Q115602.fctrYes              0.0000000
## Q115610.fctrNo               0.0000000
## Q115610.fctrYes              0.0000000
## Q115777.fctrEnd              0.0000000
## Q115777.fctrStart            0.0000000
## Q115899.fctrMe               0.0000000
## Q116197.fctrA.M.             0.0000000
## Q116197.fctrP.M.             0.0000000
## Q116441.fctrNo               0.0000000
## Q116441.fctrYes              0.0000000
## Q116448.fctrNo               0.0000000
## Q116448.fctrYes              0.0000000
## Q116601.fctrNo               0.0000000
## Q116601.fctrYes              0.0000000
## Q116797.fctrNo               0.0000000
## Q116797.fctrYes              0.0000000
## Q116881.fctrHappy            0.0000000
## Q116953.fctrNo               0.0000000
## Q116953.fctrYes              0.0000000
## Q117186.fctrCool headed      0.0000000
## Q117186.fctrHot headed       0.0000000
## Q117193.fctrOdd hours        0.0000000
## Q117193.fctrStandard hours   0.0000000
## Q118117.fctrNo               0.0000000
## Q118117.fctrYes              0.0000000
## Q118232.fctrPragmatist       0.0000000
## Q118233.fctrNo               0.0000000
## Q118233.fctrYes              0.0000000
## Q118237.fctrNo               0.0000000
## Q118237.fctrYes              0.0000000
## Q118892.fctrNo               0.0000000
## Q118892.fctrYes              0.0000000
## Q119334.fctrNo               0.0000000
## Q119334.fctrYes              0.0000000
## Q119650.fctrGiving           0.0000000
## Q119650.fctrReceiving        0.0000000
## Q119851.fctrYes              0.0000000
## Q120012.fctrNo               0.0000000
## Q120012.fctrYes              0.0000000
## Q120014.fctrNo               0.0000000
## Q120014.fctrYes              0.0000000
## Q120194.fctrTry first        0.0000000
## Q120472.fctrArt              0.0000000
## Q120650.fctrNo               0.0000000
## Q120650.fctrYes              0.0000000
## Q120978.fctrNo               0.0000000
## Q120978.fctrYes              0.0000000
## Q121011.fctrNo               0.0000000
## Q121011.fctrYes              0.0000000
## Q121699.fctrNo               0.0000000
## Q121700.fctrNo               0.0000000
## Q121700.fctrYes              0.0000000
## Q122120.fctrNo               0.0000000
## Q122769.fctrNo               0.0000000
## Q122769.fctrYes              0.0000000
## Q122770.fctrNo               0.0000000
## Q122770.fctrYes              0.0000000
## Q122771.fctrPc               0.0000000
## Q123464.fctrNo               0.0000000
## Q123464.fctrYes              0.0000000
## Q123621.fctrNo               0.0000000
## Q123621.fctrYes              0.0000000
## Q124122.fctrNo               0.0000000
## Q124122.fctrYes              0.0000000
## Q124742.fctrNo               0.0000000
## Q124742.fctrYes              0.0000000
## YOB.Age.fctr.C               0.0000000
## YOB.Age.fctr.Q               0.0000000
## YOB.Age.fctr^4               0.0000000
## YOB.Age.fctr^5               0.0000000
## YOB.Age.fctr^6               0.0000000
## YOB.Age.fctr^7               0.0000000
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId, 
            prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 97

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    1309          D                         0.2468488
## 2    1788          D                         0.2287000
## 3    1311          D                         0.2179760
## 4     892          D                         0.2694976
## 5    1393          D                                NA
## 6    4956          D                         0.2432875
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            R                             TRUE
## 2                            R                             TRUE
## 3                            R                             TRUE
## 4                            R                             TRUE
## 5                         <NA>                               NA
## 6                            R                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.7531512                               FALSE
## 2                            0.7713000                               FALSE
## 3                            0.7820240                               FALSE
## 4                            0.7305024                               FALSE
## 5                                   NA                                  NA
## 6                            0.7567125                               FALSE
##   Party.fctr.Final..rcv.glmnet.prob Party.fctr.Final..rcv.glmnet
## 1                         0.2472526                            R
## 2                         0.2575390                            R
## 3                         0.2588897                            R
## 4                         0.2602422                            R
## 5                         0.2616102                            R
## 6                         0.2618092                            R
##   Party.fctr.Final..rcv.glmnet.err Party.fctr.Final..rcv.glmnet.err.abs
## 1                             TRUE                            0.7527474
## 2                             TRUE                            0.7424610
## 3                             TRUE                            0.7411103
## 4                             TRUE                            0.7397578
## 5                             TRUE                            0.7383898
## 6                             TRUE                            0.7381908
##   Party.fctr.Final..rcv.glmnet.is.acc
## 1                               FALSE
## 2                               FALSE
## 3                               FALSE
## 4                               FALSE
## 5                               FALSE
## 6                               FALSE
##   Party.fctr.Final..rcv.glmnet.accurate Party.fctr.Final..rcv.glmnet.error
## 1                                 FALSE                         -0.4527474
## 2                                 FALSE                         -0.4424610
## 3                                 FALSE                         -0.4411103
## 4                                 FALSE                         -0.4397578
## 5                                 FALSE                         -0.4383898
## 6                                 FALSE                         -0.4381908
##      USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 803     5426          D                         0.5520684
## 1053    2969          D                         0.5223266
## 1435    4997          D                         0.5649939
## 1708    6584          D                         0.5751797
## 1979    5275          D                         0.5839014
## 2281    5638          R                         0.7186429
##      Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 803                             R                             TRUE
## 1053                            R                             TRUE
## 1435                            R                             TRUE
## 1708                            R                             TRUE
## 1979                            R                             TRUE
## 2281                            D                             TRUE
##      Party.fctr.All.X..rcv.glmnet.err.abs
## 803                             0.4479316
## 1053                            0.4776734
## 1435                            0.4350061
## 1708                            0.4248203
## 1979                            0.4160986
## 2281                            0.7186429
##      Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 803                                FALSE                         0.4926216
## 1053                               FALSE                         0.5176997
## 1435                               FALSE                         0.5429107
## 1708                               FALSE                         0.5622760
## 1979                               FALSE                         0.5932244
## 2281                               FALSE                         0.7299829
##      Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 803                             R                             TRUE
## 1053                            R                             TRUE
## 1435                            R                             TRUE
## 1708                            R                             TRUE
## 1979                            R                             TRUE
## 2281                            D                             TRUE
##      Party.fctr.Final..rcv.glmnet.err.abs
## 803                             0.5073784
## 1053                            0.4823003
## 1435                            0.4570893
## 1708                            0.4377240
## 1979                            0.4067756
## 2281                            0.7299829
##      Party.fctr.Final..rcv.glmnet.is.acc
## 803                                FALSE
## 1053                               FALSE
## 1435                               FALSE
## 1708                               FALSE
## 1979                               FALSE
## 2281                               FALSE
##      Party.fctr.Final..rcv.glmnet.accurate
## 803                                  FALSE
## 1053                                 FALSE
## 1435                                 FALSE
## 1708                                 FALSE
## 1979                                 FALSE
## 2281                                 FALSE
##      Party.fctr.Final..rcv.glmnet.error
## 803                         -0.20737840
## 1053                        -0.18230030
## 1435                        -0.15708926
## 1708                        -0.13772404
## 1979                        -0.10677564
## 2281                         0.02998287
##      USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 2412    1515          R                         0.8615325
## 2413     468          R                         0.8760584
## 2414     626          R                         0.8747618
## 2415    1236          R                         0.8820050
## 2416    2749          R                         0.8759471
## 2417    3895          R                         0.8943289
##      Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 2412                            D                             TRUE
## 2413                            D                             TRUE
## 2414                            D                             TRUE
## 2415                            D                             TRUE
## 2416                            D                             TRUE
## 2417                            D                             TRUE
##      Party.fctr.All.X..rcv.glmnet.err.abs
## 2412                            0.8615325
## 2413                            0.8760584
## 2414                            0.8747618
## 2415                            0.8820050
## 2416                            0.8759471
## 2417                            0.8943289
##      Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 2412                               FALSE                         0.8503119
## 2413                               FALSE                         0.8503529
## 2414                               FALSE                         0.8550426
## 2415                               FALSE                         0.8745067
## 2416                               FALSE                         0.8753932
## 2417                               FALSE                         0.8782998
##      Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 2412                            D                             TRUE
## 2413                            D                             TRUE
## 2414                            D                             TRUE
## 2415                            D                             TRUE
## 2416                            D                             TRUE
## 2417                            D                             TRUE
##      Party.fctr.Final..rcv.glmnet.err.abs
## 2412                            0.8503119
## 2413                            0.8503529
## 2414                            0.8550426
## 2415                            0.8745067
## 2416                            0.8753932
## 2417                            0.8782998
##      Party.fctr.Final..rcv.glmnet.is.acc
## 2412                               FALSE
## 2413                               FALSE
## 2414                               FALSE
## 2415                               FALSE
## 2416                               FALSE
## 2417                               FALSE
##      Party.fctr.Final..rcv.glmnet.accurate
## 2412                                 FALSE
## 2413                                 FALSE
## 2414                                 FALSE
## 2415                                 FALSE
## 2416                                 FALSE
## 2417                                 FALSE
##      Party.fctr.Final..rcv.glmnet.error
## 2412                          0.1503119
## 2413                          0.1503529
## 2414                          0.1550426
## 2415                          0.1745067
## 2416                          0.1753932
## 2417                          0.1782998

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "Party.fctr.Final..rcv.glmnet.prob"   
## [2] "Party.fctr.Final..rcv.glmnet"        
## [3] "Party.fctr.Final..rcv.glmnet.err"    
## [4] "Party.fctr.Final..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final..rcv.glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0 
## 3.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  data.training.all.prediction 
## 4.0000    5   0 1 1 1 
## 4.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  model.final 
## 5.0000    4   0 0 2 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 21 fit.data.training          9          1           1 468.738 478.282
## 22  predict.data.new         10          0           0 478.283      NA
##    elapsed
## 21   9.544
## 22      NA

Step 10.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.7

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.7
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 97
## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## NULL
## Loading required package: tidyr
## 
## Attaching package: 'tidyr'
## The following object is masked from 'package:Matrix':
## 
##     expand
## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"
## [1] 0.7
## [1] "glbMdlSelId: All.X##rcv#glmnet"
## [1] "glbMdlFinId: Final##rcv#glmnet"
## [1] "Cross Validation issues:"
##        MFO###myMFO_classfr  Random###myrandom_classfr 
##                          0                          0 
## Max.cor.Y.rcv.1X1###glmnet 
##                          0
##                                 max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glm                         0.5848214       0.3425094
## Max.cor.Y##rcv#rpart                   0.5633929       0.3774772
## Max.cor.Y.rcv.1X1###glmnet             0.5633929       0.3658672
## Interact.High.cor.Y##rcv#glmnet        0.5625000       0.3664097
## Low.cor.X##rcv#glmnet                  0.5553571       0.3187291
## All.X##rcv#glmnet                      0.5553571       0.3187291
## Random###myrandom_classfr              0.4696429       0.5191202
## MFO###myMFO_classfr                    0.4696429       0.5000000
## Final##rcv#glmnet                             NA              NA
##                                 max.AUCpROC.OOB max.Accuracy.fit
## All.X##rcv#glm                        0.5998195        0.5999708
## Max.cor.Y##rcv#rpart                  0.5896897        0.6000450
## Max.cor.Y.rcv.1X1###glmnet            0.5896897        0.5721673
## Interact.High.cor.Y##rcv#glmnet       0.5920549        0.5996718
## Low.cor.X##rcv#glmnet                 0.6261026        0.6218534
## All.X##rcv#glmnet                     0.6261026        0.6218534
## Random###myrandom_classfr             0.5235690        0.4700989
## MFO###myMFO_classfr                   0.5000000        0.4700989
## Final##rcv#glmnet                            NA        0.6290113
##                                 opt.prob.threshold.fit
## All.X##rcv#glm                                     0.6
## Max.cor.Y##rcv#rpart                               0.6
## Max.cor.Y.rcv.1X1###glmnet                         0.6
## Interact.High.cor.Y##rcv#glmnet                    0.6
## Low.cor.X##rcv#glmnet                              0.6
## All.X##rcv#glmnet                                  0.6
## Random###myrandom_classfr                          0.6
## MFO###myMFO_classfr                                0.5
## Final##rcv#glmnet                                  0.6
##                                 opt.prob.threshold.OOB
## All.X##rcv#glm                                     0.7
## Max.cor.Y##rcv#rpart                               0.6
## Max.cor.Y.rcv.1X1###glmnet                         0.6
## Interact.High.cor.Y##rcv#glmnet                    0.7
## Low.cor.X##rcv#glmnet                              0.7
## All.X##rcv#glmnet                                  0.7
## Random###myrandom_classfr                          0.6
## MFO###myMFO_classfr                                0.5
## Final##rcv#glmnet                                   NA
## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   R   D
##         R 494  32
##         D 466 128
##     err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy        24.52672        4.439736        29.02325              NA
## PKn        54.07667       14.275241        71.20685              NA
## N         170.90149       37.875827       210.90124              NA
## SKn       861.17518      232.827797      1104.95451              NA
## MKn       230.56787       61.957461       294.14171              NA
## SKy        62.64058       23.920386        88.27741              NA
## MKy       575.87008      132.600733       715.28313              NA
##     .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## PKy     0.01169065    0.008035714    0.007183908     52        2        8
## PKn     0.03372302    0.026785714    0.026580460    150       11       26
## N       0.08250899    0.074107143    0.073275862    367       10       92
## SKn     0.43165468    0.456250000    0.458333333   1920      105      533
## MKn     0.11600719    0.121428571    0.121408046    516       23      146
## SKy     0.03304856    0.047321429    0.046695402    147        9       56
## MKy     0.29136691    0.266071429    0.266522989   1296       41      330
##     .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy      9       35       26     10     52     10     61        0.4933040
## PKn     30      131       49     37    150     37    180        0.4758414
## N       83      230      220    102    367    102    450        0.4563353
## SKn    511     1340     1091    638   1920    638   2431        0.4556317
## MKn    136      344      308    169    516    169    652        0.4555696
## SKy     53      119       81     65    147     65    200        0.4513280
## MKy    298      752      842    371   1296    371   1594        0.4449689
##     err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy        0.4716676               NA        0.4757910
## PKn        0.3605111               NA        0.3955936
## N          0.4656716               NA        0.4686694
## SKn        0.4485287               NA        0.4545267
## MKn        0.4468370               NA        0.4511376
## SKy        0.4261264               NA        0.4413870
## MKy        0.4443442               NA        0.4487347
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##      1979.758590       507.897181      2513.788095               NA 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##         1.000000         1.000000         1.000000      4448.000000 
##         .n.New.D         .n.New.R           .n.OOB         .n.Trn.D 
##       201.000000      1191.000000      1120.000000      2951.000000 
##         .n.Trn.R           .n.Tst           .n.fit           .n.new 
##      2617.000000      1392.000000      4448.000000      1392.000000 
##           .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean 
##      5568.000000         3.232979         3.063687               NA 
## err.abs.trn.mean 
##         3.135840
## [1] "Features Importance for selected models:"
##                           All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Q109244.fctrYes                       100.00000            100.000000
## Hhold.fctrPKn                          66.66565             36.994405
## Q109244.fctrNo                         46.04154             33.886874
## Q115611.fctrYes                        42.35760             33.085721
## Q113181.fctrYes                        25.05557             25.256848
## Q113181.fctrNo                         23.63294             20.012414
## Hhold.fctrMKy                          18.99610              6.360349
## Q116881.fctrRight                      16.79152             15.718133
## Q115611.fctrNo                         16.73937             15.446845
## YOB.Age.fctr.L                         16.36530              1.810172
## Income.fctr.C                          16.10890              2.941569
## Q122771.fctrPt                         16.01287              5.479312
## Edn.fctr^4                             14.72729              0.000000
## Hhold.fctrSKy                          14.14766              0.000000
## Q119851.fctrNo                         13.90035              8.283933
## Q101163.fctrMom                        13.86636             10.565983
## Q100689.fctrYes                        13.72184              7.188095
## Q118232.fctrIdealist                   13.62430              6.922227
## Q120379.fctrYes                        13.46387              5.622551
## Q111220.fctrYes                        13.11070              0.000000
## Q101163.fctrDad                        11.65703              3.511826
## Q110740.fctrPC                         11.64944              6.301296
## Q115899.fctrCircumstances              11.14238              3.518482
## Q116881.fctrHappy                      10.17271              0.000000
## Q106997.fctrYay people!                10.15641              0.000000
## Q106389.fctrNo                         10.05999              0.439339
## Edn.fctr^7                             10.03454              0.000000
## [1] "glbObsNew prediction stats:"
## 
##    R    D 
## 1191  201
##                   label step_major step_minor label_minor     bgn     end
## 22     predict.data.new         10          0           0 478.283 492.695
## 23 display.session.info         11          0           0 492.696      NA
##    elapsed
## 22  14.412
## 23      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                        label step_major step_minor label_minor     bgn
## 2               inspect.data          2          0           0  24.134
## 14   partition.data.training          6          0           0 199.188
## 16                fit.models          8          0           0 311.190
## 17                fit.models          8          1           1 368.632
## 3                 scrub.data          2          1           1 162.974
## 20         fit.data.training          9          0           0 438.901
## 22          predict.data.new         10          0           0 478.283
## 1                import.data          1          0           0  10.210
## 18                fit.models          8          2           2 422.512
## 21         fit.data.training          9          1           1 468.738
## 19                fit.models          8          3           3 434.376
## 15           select.features          7          0           0 306.793
## 11      extract.features.end          3          6           6 197.269
## 12       manage.missing.data          4          0           0 198.181
## 13              cluster.data          5          0           0 199.079
## 9      extract.features.text          3          4           4 197.132
## 10   extract.features.string          3          5           5 197.200
## 7     extract.features.image          3          2           2 197.042
## 4             transform.data          2          2           2 196.937
## 6  extract.features.datetime          3          1           1 197.003
## 8     extract.features.price          3          3           3 197.096
## 5           extract.features          3          0           0 196.981
##        end elapsed duration
## 2  162.973 138.839  138.839
## 14 306.792 107.604  107.604
## 16 368.631  57.442   57.441
## 17 422.512  53.880   53.880
## 3  196.936  33.962   33.962
## 20 468.738  29.837   29.837
## 22 492.695  14.412   14.412
## 1   24.134  13.924   13.924
## 18 434.376  11.864   11.864
## 21 478.282   9.544    9.544
## 19 438.901   4.525    4.525
## 15 311.190   4.397    4.397
## 11 198.180   0.912    0.911
## 12 199.078   0.897    0.897
## 13 199.188   0.109    0.109
## 9  197.200   0.068    0.068
## 10 197.268   0.068    0.068
## 7  197.095   0.054    0.053
## 4  196.980   0.043    0.043
## 6  197.041   0.038    0.038
## 8  197.131   0.036    0.035
## 5  197.002   0.021    0.021
## [1] "Total Elapsed Time: 492.695 secs"